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In today's fast-paced digital landscape, the ability to monitor and observe the health and performance of applications and infrastructure is not just beneficial—it's essential. As systems grow increasingly complex and the volume of data continues to skyrocket, organizations are faced with the challenge of not just managing this information but making sense of it. This is where Grafana steps in. In this blog post, we'll take a comprehensive look at what Grafana is and how it works. Let's get started! What is Grafana?Grafana is an open-source visualization and monitoring platform developed by Grafana Labs. It allows you to query, visualize, alert on, and understand your data from various data sources through highly customizable dashboards. Here are three key reasons why Grafana has gained significant popularity and widespread adoption among organizations of all sizes and industries: Fast: Grafana is known for its exceptional performance. The backend of Grafana is powered by Go, a programming language renowned for its speed and efficiency. Go's lightweight nature and native compilation enable Grafana to handle large volumes of data and render visualizations quickly. This means that even when dealing with massive datasets and complex dashboards, Grafana remains responsive and provides a smooth user experience.Versatile: Grafana follows a plugin architecture, which allows users to extend its functionality and integrate with a wide range of data sources. Whether you are working with NoSQL/SQL databases, project management tools like Jira, or CI/CD tools like GitLab, Grafana has you covered. Beyond data source plugins, Grafana also supports panel plugins for custom visualization types and app plugins that add new features and integrate applications directly into the Grafana ecosystem. This extensive collection of plugins ensures that Grafana can seamlessly integrate with your existing infrastructure and provide a unified view of your data. Open-source: Grafana is an open-source software. This means that you have complete access to the source code, allowing you to inspect, modify, and contribute to the project. The open-source nature of Grafana fosters a vibrant community of developers and users who actively collaborate to improve the platform. This community-driven approach ensures that Grafana remains at the forefront of innovation, with regular updates, bug fixes, and new features being added continuously. Additionally, being open-source eliminates vendor lock-in and gives you the freedom to customize Grafana to fit your specific requirements. Grafana Use CasesHere are some of the most common use cases of Grafana: Infrastructure monitoring Grafana is widely used to monitor IT infrastructure, including servers, networks, and storage systems. It can aggregate metrics such as CPU usage, memory utilization, disk I/O, and network traffic from various infrastructure components, offering a unified view of system health and performance. Furthermore, Grafana enables the setup of alerts based on predefined thresholds for these metrics. For instance, you can configure an alert to be triggered if CPU usage exceeds 80% for more than five minutes. By tracking resource utilization over time, Grafana aids in identifying trends and forecasting future infrastructure needs. To learn how to monitor infrastructure using the popular Grafana-Prometheus stack, check out our blog post: What Is Grafana & How to Use Grafana-Prometheus Stack for Monitoring? Application performance monitoring (APM)Grafana is a popular choice for monitoring the performance and health of applications, particularly in microservices architectures. A key APM use case is request tracing, where Grafana ingests distributed tracing data to visualize the end-to-end flow of requests through multiple services. This visualization aids in identifying bottlenecks and debugging latency issues. Error tracking is another crucial aspect of APM. Grafana excels at correlating logs and metrics, quickly identifying and diagnosing application errors and exceptions. When an issue arises, developers can view the relevant logs and metrics in context, making it easier to pinpoint the root cause and resolve the problem. User experience monitoring is also critical for ensuring application success. Grafana can track key frontend performance metrics, such as page load times, user journeys, and conversion rates. By visualizing this data in real-time, teams can identify potential issues before they impact users and make data-driven decisions to optimize the user experience. Business intelligence and analytics While often thought of as a tool primarily for technical users, Grafana is increasingly being adopted for business intelligence and analytics. This includes applications such as sales & marketing dashboards, IoT analytics, and financial reporting. Grafana can connect to both SQL and NoSQL databases to visualize key business metrics such as revenue, customer acquisition costs, and churn rates. Companies across various industries, including manufacturing, logistics, and utilities, utilize Grafana to analyze sensor data and monitor KPIs related to equipment uptime, asset utilization, and predictive maintenance. Thanks to its rich visualization capabilities, Grafana is also well-suited for creating executive dashboards and shareholder reports. Grafana Core Components: Dashboards and PanelsAt the heart of Grafana’s user interface are dashboards and panels. Dashboards provide a visual representation of data and are composed of individual panels arranged in a grid. The following image illustrates a sample Grafana dashboard that provides a comprehensive snapshot of website performance metrics: Example Grafana dashboardPanels are the building blocks of a Grafana dashboard, serving as containers for visualizing data. In the example dashboard above, there are nine distinct panels (highlighted in yellow) displaying various metrics and data points. Panels offer a wide range of visualization formats to present data in meaningful ways, such as: Time series graphsStats and gaugesTablesHeatmaps and histogramsAlert listsAnd many more...Each panel can display data from one or more data sources, enabling you to combine and correlate metrics from different systems in a single view. One of the key strengths of panels is their deep customization options. Beyond choosing data sources and visualization types, panels provide a rich set of configuration settings to fine-tune every aspect of their appearance and behavior. Some common panel customization options include: Queries and data transformations: Grafana's query editor allows you to extract and manipulate data from each data source. This enables complex data transformations to be performed on the data before visualization.Display options: Grafana provides various options to customize the appearance of panels. You can adjust the panel's size, title, background, borders, and other visual properties to achieve the desired look and feel.Field and override options: You can dynamically set colors, thresholds, value mappings, links, and more based on the data being displayed.Thresholds and alerts: You can define thresholds on the data to set boundaries for specific values or ranges. Additionally, you can configure rules that trigger alerts when certain conditions are met.By leveraging these customization options, you can create highly tailored, informative, and interactive dashboards that provide valuable insights into your systems and infrastructure. How Grafana Works: From Data Source to DashboardIn Grafana, the process of getting data from a data source and displaying it on a dashboard involves three main steps: #1 Data Source PluginA data source plugin in Grafana is a connector that allows Grafana to communicate with a specific data source. Grafana supports various types of data sources, such as databases (e.g., MySQL, PostgreSQL), time series databases (e.g., Prometheus, InfluxDB), cloud services (e.g., AWS CloudWatch, Google Cloud Monitoring), and more. Each data source has its own plugin that defines how Grafana interacts with it, This includes how to establish a connection, authenticate, and retrieve data. Given that each data source can have its own query language, authentication method, and data format, the plugin plays a crucial role in reconciling these differences. It understands the specifics of the data source and translates requests from Grafana’s query editor into queries that the data source comprehends. Once the data is retrieved, the plugin converts it into a data frame, a unified data structure used by Grafana to standardize and represent data internally. The plugin acts as the first step in the data transformation process, enabling Grafana to connect to and fetch data from the desired data source. #2 QueryOnce Grafana is connected to a data source through the plugin, you need to specify a query to retrieve the desired data. A query is a request for specific data from the data source. It defines what data you want to retrieve and how you want to filter or aggregate it. The query language and syntax may vary depending on the data source. For example, SQL databases use SQL queries, while Prometheus uses its own query language called PromQL. The query acts as the second step, allowing you to select and filter the data you want to visualize in your dashboard. #3 Transformation (optional)After the data is retrieved from the data source using the query, you have the option to apply transformations to the data before it is visualized on the dashboard. Transformations are operations that modify or enhance the queried data. They allow you to perform calculations, aggregations, filtering, or other manipulations on the data. Grafana provides a set of built-in transformations, such as renaming fields, filtering rows, joining data from multiple queries, calculating new fields, and more. The transformation step acts as the third and final step, enabling you to refine and customize the data before it is displayed on the dashboard. After the data passes through these three steps (data source plugin, query, and optional transformation), it is ready to be visualized on the Grafana dashboard. Grafana LGTM StackThe Grafana LGTM stack is an opinionated observability stack developed by Grafana Labs. LGTM stands for Loki, Grafana, Tempo, and Mimir, which are the key components of this stack. The LGTM stack aims to provide a unified solution for monitoring and troubleshooting modern applications and infrastructure by addressing the three pillars of observability: logs, metrics, and traces. To understand how each component contributes to the stack, let's take a closer look: Loki: A horizontally scalable and cost-effective log aggregation system designed to store and query logs from all your applications and infrastructure. It integrates seamlessly with Grafana, allowing users to query and visualize log data alongside metrics and traces.Want to learn more about Loki and how to use it to gather logs from your Kubernetes cluster and applications running on it? Check out the Grafana Loki course from KodeKloud. Grafana: The centerpiece of the LGTM stack. As discussed earlier, it provides a powerful and flexible platform for visualizing and analyzing data from various sources, including Loki, Tempo, and Mimir.Tempo: A distributed tracing system that enables developers to visualize the end-to-end flow of requests through a microservices architecture. By integrating with Grafana, Tempo helps identify performance bottlenecks, debug latency issues, and understand how different services interact.Mimir: A highly scalable, multi-tenant, time series database for long-term storage for Prometheus metrics. It allows users to store and query large amounts of metric data efficiently, making it an essential component of the LGTM stack.By combining these components, the LGTM stack provides a comprehensive and integrated observability solution. It allows you to collect, store, and analyze large volumes of logs, metrics, and traces without the complexity of managing multiple tools. ConclusionIn this blog post, we explored what Grafana is, its key use cases, and why it has become the preferred tool for organizations of all sizes across various industries for their visualization and monitoring needs. We also discussed panels and dashboards, the core components of Grafana, and the three steps —plugin, query, and transform—that data undergoes from a data source before being displayed on a dashboard. Finally, we looked at the Grafana Lab's LGTM stack, where Grafana serves as the central hub, aggregating and visualizing logs from Loki, metrics from Mimir, and traces from Tempo. Now, you should have a thorough understanding of what Grafana is and how it works. Practice monitoring infrastructure using the Grafana-Prometheus stack using KodeKloud’s Grafana and Prometheus playground. Want to master observability? Check out the Prometheus Certified Associate (PCA) course from KodeKloud. View the full article

Almost all companies today are “data rich.” They have access to exponentially more data than ever before. But they are still information poor, struggling to make sense of it all. One of the main reasons for this is disconnected data silos, acting as barriers that prevent a 360-degree view of their business. Data integration is […]View the full article

The Payment Card Industry Data Security Standard (PCI DSS) is a critical ally, providing a robust blueprint for protecting sensitive data. Our comprehensive blog delves into the deep understanding of PCI DSS, exploring its foundational principles and the specific requirements it imposes on entities that handle cardholder data. Whether you’re a small business owner, a […] The post PCI DSS (Payment Card Industry Data Security Standard) first appeared on StrongBox IT. The post PCI DSS (Payment Card Industry Data Security Standard) appeared first on Security Boulevard. View the full article

What is OpenShift? What is OpenShift OpenShift is a family of containerization software products made by Red Hat. The most popular offering is OpenShift Container Platform, a hybrid cloud platform as a service (PaaS) built around Linux containers. This platform utilizes Kubernetes for container orchestration and management, with Red Hat Enterprise Linux as the foundation. Key features of OpenShift include: Automated deployment and scaling: Streamlines app development and deployment across different environments. Integrated security: Provides built-in security features for workloads and infrastructure. Multi-cloud and on-premise support: Deploy applications on various cloud platforms (AWS, Azure, GCP) or on-premises infrastructure. Developer-friendly tools: Offers various tools for development, CI/CD pipelines, and application monitoring. Large ecosystem of partners and integrations: Extends functionalities with numerous tools and technologies. Top 10 use cases of OpenShift? Top 10 Use Cases of OpenShift: Modernizing legacy applications: Refactor and containerize existing applications for improved scalability and portability. Building cloud-native microservices: Develop and deploy applications composed of interconnected, independent services. Continuous integration and continuous delivery (CI/CD): Automate build, test, and deployment processes for faster development cycles. Edge computing: Deploy applications closer to data sources for faster processing and reduced latency. Data science and machine learning: Develop and manage data pipelines and machine learning models. Internet of Things (IoT): Build and manage applications for connected devices and sensors. High-performance computing (HPC): Run resource-intensive scientific and engineering applications. Internal developer platforms: Create centralized platforms for internal application development within organizations. Software supply chain management: Securely manage and track software builds and deployments. Containerized DevOps environments: Establish consistent and secure environments for development and operations teams. These are just some of the many use cases for OpenShift. It’s a versatile platform that can be adapted to various needs and industries. What are the feature of OpenShift? OpenShift boasts a wide range of features that cater to developers, operators, and businesses alike. Here are some of the key capabilities: Developer-Centric Features: Integrated CI/CD Pipelines: Seamlessly automate building, testing, and deploying applications with Tekton and other CI/CD tools. Multi-Language Support: Develop with various languages like Java, Python, Node.js, Go, and Ruby. Command-Line and IDE Integrations: Work comfortably with tools like Git, VS Code, and Red Hat CodeReady Studio. Source-to-Image Building: Simplify container image creation directly from your application code. Built-in Monitoring and Logging: Gain insights into application performance and health with pre-configured monitoring and logging tools. Operational Features: Automated Installation and Upgrades: Streamline infrastructure management with automated setups and updates. Centralized Policy Management: Enforce consistent security and governance across application deployments. Multi-Cluster Management: Efficiently manage deployments across multiple OpenShift clusters. Self-Service Environments: Empower developers with on-demand access to approved resources. Operator Framework: Extend functionality with pre-built operators for databases, networking, and more. Security and Compliance Features: Integrated Security Scanning: Scan container images for vulnerabilities before deployment. Role-Based Access Control (RBAC): Granularly control user access to resources. Network Policies and Security Context Constraints: Enforce specific security configurations on applications. Compliance Support: Align deployments with compliance frameworks like HIPAA, PCI DSS, and SOC 2. Red Hat Support: Benefit from industry-leading support for deployments. Additional Features: Scalability: Easily scale applications up or down based on demand. High Availability: Ensure application uptime with disaster recovery and failover mechanisms. Portability: Deploy applications across diverse environments, including public clouds, private clouds, and on-premises infrastructure. Large Ecosystem: Leverage a vast ecosystem of tools, integrations, and partner solutions. This list is not exhaustive, and the specific features available may vary depending on the OpenShift version you choose. How OpenShift works and Architecture? OpenShift works and Architecture OpenShift boasts a wide range of features that cater to developers, operators, and businesses alike. Here are some of the key capabilities: Developer-Centric Features: Integrated CI/CD Pipelines: Seamlessly automate building, testing, and deploying applications with Tekton and other CI/CD tools. Multi-Language Support: Develop with various languages like Java, Python, Node.js, Go, and Ruby. Command-Line and IDE Integrations: Work comfortably with tools like Git, VS Code, and Red Hat CodeReady Studio. Source-to-Image Building: Simplify container image creation directly from your application code. Built-in Monitoring and Logging: Gain insights into application performance and health with pre-configured monitoring and logging tools. Operational Features: Automated Installation and Upgrades: Streamline infrastructure management with automated setups and updates. Centralized Policy Management: Enforce consistent security and governance across application deployments. Multi-Cluster Management: Efficiently manage deployments across multiple OpenShift clusters. Self-Service Environments: Empower developers with on-demand access to approved resources. Operator Framework: Extend functionality with pre-built operators for databases, networking, and more. Security and Compliance Features: Integrated Security Scanning: Scan container images for vulnerabilities before deployment. Role-Based Access Control (RBAC): Granularly control user access to resources. Network Policies and Security Context Constraints: Enforce specific security configurations on applications. Compliance Support: Align deployments with compliance frameworks like HIPAA, PCI DSS, and SOC 2. Red Hat Support: Benefit from industry-leading support for deployments. Additional Features: Scalability: Easily scale applications up or down based on demand. High Availability: Ensure application uptime with disaster recovery and failover mechanisms. Portability: Deploy applications across diverse environments, including public clouds, private clouds, and on-premises infrastructure. Large Ecosystem: Leverage a vast ecosystem of tools, integrations, and partner solutions. How to Install OpenShift it? Installing OpenShift can be done in several ways, depending on your needs and environment. Here are the three main options: 1. OpenShift Local: Pros: Quick and easy to set up, ideal for individual developers and learning. Cons: Not suitable for production use, limited resources. Installation Steps: Download the crc tool: Go to the Red Hat Console official site and create a free Red Hat Developer account. Download the crc tool for your system. Set up the virtual machine: Run crc setup and follow the instructions. This downloads and configures a virtual machine that will host your OpenShift cluster. Start the cluster: Run crc start. Access the cluster: You can access the OpenShift web console at https://127.0.0.1:8443/console. 2. User-Provisioned Infrastructure: Pros: More control over the infrastructure, suitable for small-scale production use. Cons: Requires technical expertise to manage the infrastructure. Installation Steps: Prepare your infrastructure: Set up servers with the required operating system and network configuration. Download the installation program: Get the appropriate installer from the OpenShift Cluster Manager site. Generate installation manifests: Run the installer with options specific to your infrastructure and desired configuration. Deploy the cluster: Follow the generated instructions to provision and deploy the OpenShift cluster on your infrastructure. 3. Managed OpenShift: Pros: No infrastructure management required, easiest and quickest to set up. Cons: Less control over the environment, potential costs involved. Options: OpenShift Online: Managed OpenShift service from Red Hat. Amazon Red Hat OpenShift (ARO): Managed OpenShift service on AWS. Other cloud providers: Many cloud providers offer similar managed OpenShift services (e.g., Microsoft Azure Red Hat OpenShift). Installation Steps: Choose a provider: Select the desired managed OpenShift service based on your needs and budget. Create an account: Register for an account with the chosen provider. Provision the cluster: Follow the provider’s specific instructions to create a new OpenShift cluster. Access the cluster: The provider will provide access details to your managed OpenShift cluster. Notes: The specific installation steps and options may vary depending on your chosen platform and version of OpenShift. Consider your technical expertise, project requirements, and budget when choosing an installation method. Basic Tutorials of OpenShift: Getting Started Basic Tutorials of OpenShift OpenShift offers various installation methods, and the approach you choose will depend on your needs and technical expertise. Here are some different options with step-by-step tutorials: 1. OpenShift Local (developer sandbox): Pros: Quick and easy setup, ideal for learning and individual developers. Cons: Not suitable for production use, limited resources. Steps: Setup: Create a free Red Hat Developer account from the Red Hat official site. Download and install the crc tool based on your operating system. Start the cluster: Run crc setup and follow the instructions to download and configure a virtual machine for your OpenShift cluster. Run crc start to launch the cluster. Access the cluster: Access the OpenShift web console at https://127.0.0.1:8443/console. 2. Minishift (local Kubernetes for OpenShift development): Pros: Lightweight, good for learning OpenShift development workflows. Cons: Simulates OpenShift on a single node, not suitable for production. Steps: Setup: Install Minishift based on your operating system: [<invalid URL removed>] Configure Minishift with desired memory and storage allocations. Start the cluster: Run minishift start to create and start a local Kubernetes cluster with OpenShift features. Access the cluster: Open the Kubernetes dashboard at https://localhost:8443/console. 3. User-Provisioned Infrastructure: Pros: More control over the infrastructure, suitable for small-scale production use. Cons: Requires technical expertise to manage the infrastructure. Steps: Prepare your infrastructure: Set up servers with the required operating system and network configuration. Download the installation program: Get the installer from the OpenShift Cluster Manager site. Generate installation manifests: Run the installer with options specific to your infrastructure and desired configuration. Deploy the cluster: Follow the generated instructions to provision and deploy the OpenShift cluster on your infrastructure. 4. Managed OpenShift: Pros: No infrastructure management required, easiest and quickest to set up. Cons: Less control over the environment, potential costs involved. Options: Other cloud providers: Many cloud providers offer similar managed OpenShift services (e.g., Microsoft Azure Red Hat OpenShift). Steps: Choose a provider: Select the desired managed OpenShift service based on your needs and budget. Generate an account: Register for an account with the chosen provider. Provision the cluster: Follow the provider’s specific instructions to create a new OpenShift cluster. Access the cluster: The provider will provide access details to your managed OpenShift cluster. The post What is OpenShift and use cases of OpenShift? appeared first on DevOpsSchool.com. View the full article

What is Tensorflow? What is Tensorflow TensorFlow is an open-source machine learning (ML) framework developed by the Google Brain team. It is designed to facilitate the development and deployment of machine learning models, particularly deep learning models. TensorFlow provides a comprehensive set of tools and libraries for building and training a wide range of machine learning models, from simple linear models to complex neural networks. Key Features of TensorFlow: Flexible Architecture: TensorFlow allows users to define, train, and deploy machine learning models across a variety of platforms and devices. Data Flow Graphs: TensorFlow represents computations using data flow graphs, where nodes in the graph represent operations, and edges represent data flowing between operations. Wide Range of Support: TensorFlow supports various machine learning tasks, including classification, regression, clustering, natural language processing (NLP), computer vision, and more. Neural Network Support: TensorFlow has extensive support for deep learning and neural networks, making it particularly powerful for tasks such as image recognition, speech recognition, and natural language understanding. TensorBoard: TensorBoard is a visualization tool that comes with TensorFlow, allowing users to monitor and visualize the training process, model graphs, and various metrics. TensorFlow Lite: TensorFlow Lite is a lightweight version of TensorFlow designed for mobile and embedded devices, enabling the deployment of machine learning models on edge devices. Highly Scalable: TensorFlow can scale from running on a single device to distributed systems, making it suitable for both small-scale and large-scale machine learning tasks. Community and Ecosystem: TensorFlow has a large and active community, contributing to a rich ecosystem of pre-trained models, libraries, and tools that can be used in conjunction with TensorFlow. What is top use cases of Tensorflow? Top Use Cases of TensorFlow: Image Recognition and Classification: TensorFlow is widely used for image recognition tasks, including image classification, object detection, and image segmentation. Natural Language Processing (NLP): TensorFlow is applied to tasks such as language translation, sentiment analysis, text summarization, and language modeling. Speech Recognition: TensorFlow is used for developing speech recognition systems, enabling applications like voice assistants and transcription services. Recommendation Systems: TensorFlow is employed in building recommendation systems for personalized content delivery, such as movie recommendations and product recommendations. Healthcare and Medical Imaging: TensorFlow is utilized in medical image analysis for tasks like tumor detection, disease diagnosis, and medical image segmentation. Time Series Analysis: TensorFlow is applied to time series data for tasks such as financial forecasting, stock price prediction, and energy consumption forecasting. Generative Models: TensorFlow is used for training generative models, including Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), for tasks like image synthesis. Autonomous Vehicles: TensorFlow is employed in developing models for autonomous vehicles, including object detection, lane detection, and decision-making algorithms. Anomaly Detection: TensorFlow is used for anomaly detection in various domains, such as fraud detection in finance or fault detection in industrial systems. Reinforcement Learning: TensorFlow is applied to reinforcement learning tasks, including training agents for playing games, robotic control, and optimization problems. TensorFlow’s versatility, scalability, and extensive community support make it a go-to framework for a broad range of machine learning applications. Its ability to handle both research and production-level projects has contributed to its widespread adoption in academia and industry. What are feature of Tensorflow? Features of Tensorflow Features of TensorFlow: Comprehensive Machine Learning Library: TensorFlow offers a comprehensive set of tools and libraries for machine learning tasks, covering a wide range of applications from traditional machine learning to deep learning. Neural Network Support: TensorFlow is particularly powerful in building and training neural networks, making it a leading choice for deep learning applications. TensorBoard Visualization: TensorBoard, a built-in tool, allows users to visualize model graphs, monitor training progress, and explore model performance metrics. Data Flow Graphs: TensorFlow represents computations using data flow graphs, offering a flexible and efficient way to express complex mathematical operations. TensorFlow Lite: TensorFlow Lite is a lightweight version designed for mobile and edge devices, enabling the deployment of models on resource-constrained platforms. Highly Scalable: TensorFlow can scale from running on a single device to distributed systems, making it suitable for both small-scale and large-scale machine learning tasks. Keras Integration: TensorFlow integrates with the high-level neural networks API, Keras, providing a user-friendly interface for building and training neural networks. AutoGraph: AutoGraph is a feature of TensorFlow that automatically converts Python functions into TensorFlow graphs, simplifying the process of creating and optimizing models. Eager Execution: TensorFlow supports eager execution, allowing for immediate evaluation of operations, making it easier to debug and experiment with models. Community and Ecosystem: TensorFlow has a large and active community, contributing to an extensive ecosystem of pre-trained models, libraries, and tools. What is the workflow of Tensorflow? The workflow of using TensorFlow typically involves the following steps: Installation: Install TensorFlow on your machine using the appropriate version and installation method (e.g., pip for Python). Define Model Architecture: Choose or design a model architecture for your specific task. Define the layers, connections, and activation functions. Data Preparation: Prepare the training, validation, and test datasets. Ensure the data is formatted correctly and preprocessed as needed. Model Compilation: Assemble the model by specifying the optimizer, loss function, and evaluation metrics. This step designs the model for training. Model Training: Train the model using the training dataset. Apply the compiled model along with the training data to adjust the model’s parameters. Model Evaluation: Evaluate the model’s performance on the validation or test dataset using appropriate metrics. This helps evaluates how well the model generalizes to unseen data. Fine-Tuning and Hyperparameter Tuning: Iterate on the model architecture, hyperparameters, and training process based on the evaluation results. Fine-tune the model for better performance. TensorBoard Visualization: Use TensorBoard to visualize the model graph, monitor training metrics, and analyze performance. This step aids in debugging and optimizing the model. Model Deployment: Once satisfied with the model, deploy it for inference. This may involve exporting the model to TensorFlow SavedModel format or converting it to TensorFlow Lite for deployment on mobile or edge devices. Integration with Applications: Integrate the trained model with the target application, whether it’s a web application, mobile app, or embedded system. Ensure that the inference process aligns with the deployment requirements. Monitoring and Maintenance: Monitor the model’s performance in real-world scenarios and make necessary updates or retraining as needed. This step ensures that the model continues to perform well over time. TensorFlow’s workflow can be adapted based on the specific needs of the project, the type of model being developed, and the application’s deployment requirements. The flexibility and scalability of TensorFlow make it suitable for a wide range of machine learning tasks and projects. How Tensorflow Works & Architecture? Tensorflow Works & Architecture TensorFlow is a powerful open-source framework for developing and deploying machine learning (ML) models, particularly those leveraging deep learning. Its architecture revolves around three key components: 1. Data Flow Graphs: TensorFlow constructs computations as directed graphs, where nodes represent operations (e.g., matrix multiplication, activation functions) and edges represent data tensors flowing between them. This allows for clear visualization and efficient execution of complex computations. 2. Tensors: Tensors are multi-dimensional arrays containing data like images, text, or numerical values. They serve as the input and output of operations in the data flow graph. TensorFlow supports various data types for tensors, enabling flexibility in handling different kinds of data. 3. Eager Execution and Symbolic Execution: TensorFlow provides two execution modes: Eager Execution: Executes operations immediately as they are defined, offering a more interactive and flexible approach for experimenting and debugging. Symbolic Execution: Creates the data flow graph without immediate execution, allowing for optimization and efficient deployment on various platforms. Benefits of TensorFlow Architecture: Modular and Scalable: The data flow graph allows for building complex models by combining modular operations. Automatic Differentiation: TensorFlow automatically calculates gradients for backpropagation, simplifying training of deep learning models. Multiple Execution Modes: Provides flexibility for development and deployment across different platforms. Rich Ecosystem: Extensive documentation, tutorials, and community support facilitate learning and development. By understanding the core principles of TensorFlow’s architecture, you can leverage its strengths to build and deploy powerful machine learning models for diverse applications. How to Install and Configure Tensorflow? Following are the general steps to install and configure TensorFlow: 1. Choose Your Installation Method: TensorFlow for CPU: Install using pip: pip install tensorflow TensorFlow with GPU support (requires NVIDIA GPU): Install using pip: pip install tensorflow-gpu TensorFlow in a virtual environment: Create a virtual environment using virtualenv or conda to isolate dependencies. TensorFlow from source: For advanced users or specific needs, build from source code. 2. Verify Installation: Open a Python interpreter and run: import tensorflow as tf If successful, you’ll see the TensorFlow version without errors. 3. Additional Configuration (Optional): GPU Configuration: If using a GPU, ensure proper drivers and CUDA Toolkit are installed and configured. Alternative Environments: For cloud-based or Jupyter Notebook environments, follow specific setup instructions. Specific Guides: Windows: Install Tensorflow pip of windows version from their official website. macOS: Install Tensorflow pip of macOS version from their official website Linux: Install Tensorflow pip of Linux version from their official website GPU Support: Install Tensorflow gpu of windows version from their official website. Troubleshooting: Consult the TensorFlow documentation and forums for troubleshooting tips. Search for solutions online in the vast TensorFlow community. Important Tips: Consider using virtual environments to manage dependencies and avoid conflicts. Keep your TensorFlow installation up-to-date for bug fixes and new features. Explore TensorFlow extensions like TensorFlow Hub for pre-trained models and tools. Leverage community resources for learning and support. If you encounter any issues, provide more details about your environment (OS, Python version, GPU details) for tailored guidance. Fundamental Tutorials of Tensorflow: Getting started Step by Step Fundamental Tutorials of Tensorflow Following are some step-by-step fundamental tutorials to get you started with TensorFlow: 1. Hello, TensorFlow!: Import TensorFlow: import tensorflow as tf Create a constant tensor: hello = tf.constant('Hello, TensorFlow!') Print the tensor: print(hello) Run the session: sess = tf.Session() (if using older versions of TensorFlow) Evaluate the tensor: print(sess.run(hello)) 2. Basic Operations: Create tensors: a = tf.constant(3), b = tf.constant(4) Add tensors: c = tf.add(a, b) Multiply tensors: d = tf.multiply(a, b) Run the session and evaluate: print(sess.run(c), sess.run(d)) 3. Working with Variables: Create a variable: my_var = tf.Variable(0) Initialize variables: init = tf.global_variables_initializer() Run initialization: sess.run(init) Assign a new value: update = tf.assign(my_var, 10) Run the update: sess.run(update) Print the variable’s value: print(sess.run(my_var)) 4. Linear Regression: Generate sample data Define placeholders for inputs and outputs Create variables for weights and biases Define the linear model Define a loss function (e.g., mean squared error) Use an optimizer to minimize the loss (e.g., gradient descent) Train the model by feeding data in batches Evaluate model performance on test data 5. Simple Neural Network: Construct a multi-layer perceptron (MLP) with hidden layers Use activation functions (e.g., ReLU) for non-linearity Apply softmax for classification tasks Train the network using backpropagation Important Notes: Start with easy examples and gradually improve to more complex ones. Use print statements and visualizations to track progress and understand model behavior. Experiment with different hyperparameters (learning rate, batch size, etc.) to optimize performance. Leverage community resources and seek help when needed. Practice regularly to solidify your TensorFlow skills. The post What is Tensorflow and use cases of Tensorflow? appeared first on DevOpsSchool.com. View the full article

What is Visual Studio? What is Visual Studio Visual Studio is an integrated development environment (IDE) developed by Microsoft. It provides a comprehensive set of tools and features for software development, making it a popular choice for developers working on various platforms and using different programming languages. Visual Studio supports languages such as C#, Visual Basic, C++, F#, Python, and more. It includes a code editor, debugger, profiler, and various other tools to assist developers in building, debugging, testing, and deploying applications. Key Features of Visual Studio: Code Editor: Visual Studio includes a powerful code editor with features such as syntax highlighting, IntelliSense (code completion), and code navigation. Debugger: The integrated debugger allows developers to set breakpoints, inspect variables, and step through code for efficient debugging. Visual Designers: Visual Studio includes visual designers for building graphical user interfaces (GUIs) for Windows applications, web applications, and mobile apps. Integrated Git Support: Visual Studio integrates with Git, providing tools for version control, branching, merging, and collaborating with team members. NuGet Package Manager: NuGet is a package manager for .NET that is integrated into Visual Studio. It allows developers to easily manage and install third-party libraries and packages. Unit Testing: Visual Studio supports unit testing with built-in testing frameworks, making it easier for developers to write and run tests for their code. Azure Integration: Visual Studio integrates with Microsoft Azure, facilitating the development, deployment, and management of cloud-based applications. Code Analysis and Refactoring: Visual Studio includes code analysis tools that help identify potential issues and code refactoring features for improving code quality and maintainability. Performance Profiling: Developers can use performance profiling tools to analyze the performance of their applications, identify bottlenecks, and optimize code. Cross-Platform Development: Visual Studio supports cross-platform development for various platforms, including Windows, macOS, Linux, Android, and iOS. Extensions and Marketplace: Visual Studio can be extended with a wide range of extensions available in the Visual Studio Marketplace, providing additional functionality and tools. Container Tools: Visual Studio includes tools for working with containers, making it easier for developers to build and deploy containerized applications. Web Development Tools: Visual Studio provides tools for web development, including support for popular web frameworks, client-side libraries, and debugging tools for web applications. Game Development: Visual Studio supports game development with tools and templates for game engines such as Unity and Unreal Engine. What is top use cases of Visual Studio? Top Use Cases of Visual Studio: .NET Development: Visual Studio is a primary IDE for developing applications using the .NET framework, including desktop applications, web applications, and services. C++ Development: Visual Studio is widely used for C++ development, providing a feature-rich environment for building Windows applications, game development, and more. C# Development: C# developers commonly use Visual Studio for building Windows applications, web applications using ASP.NET, and cross-platform applications using Xamarin. Web Development: Visual Studio is a popular choice for web development with support for HTML, CSS, JavaScript, and frameworks like ASP.NET and Node.js. Mobile App Development: Visual Studio supports mobile app development for iOS and Android platforms using Xamarin, allowing developers to write cross-platform mobile applications. Azure Cloud Development: Developers working with Microsoft Azure use Visual Studio for building, deploying, and managing cloud-based applications and services. Game Development with Unity: Visual Studio is integrated with the Unity game development engine, making it a preferred IDE for Unity game developers. Python Development: Visual Studio supports Python development with features like IntelliSense, debugging, and integration with popular Python frameworks. Artificial Intelligence and Machine Learning: Visual Studio provides tools for developing applications and solutions using artificial intelligence (AI) and machine learning (ML) frameworks. Internet of Things (IoT) Development: Visual Studio supports IoT development with tools for building applications for embedded systems and devices. Cross-Platform Development: Developers use Visual Studio for cross-platform development, targeting various operating systems and platforms with a single codebase. Database Development: Visual Studio includes tools for database development, schema management, and data manipulation, making it suitable for database-centric applications. Visual Studio’s versatility, extensive features, and support for multiple programming languages make it a preferred IDE for a wide range of development scenarios, from enterprise-level applications to small-scale projects. What are feature of Visual Studio? Visual Studio is a powerful integrated development environment (IDE) with a wide range of features to support software development. Here are some key features: Code Editor: Visual Studio includes a sophisticated code editor with features like syntax highlighting, IntelliSense (code completion), code navigation, and real-time error checking. Debugger: The integrated debugger allows developers to set breakpoints, step through code, inspect variables, and analyze the runtime behavior of applications. Visual Designers: Visual Studio provides visual designers for building graphical user interfaces (GUIs) for various application types, including Windows Forms, WPF, ASP.NET, and Xamarin. Git Integration: Visual Studio integrates with Git, a popular version control system, providing tools for version control, branching, merging, and collaboration. NuGet Package Manager: NuGet is integrated into Visual Studio, allowing developers to manage and install third-party libraries and packages easily. Unit Testing: Visual Studio supports unit testing with built-in testing frameworks, enabling developers to write, run, and manage tests for their code. Azure Integration: Visual Studio integrates with Microsoft Azure, making it easy for developers to build, deploy, and manage cloud-based applications. Code Analysis and Refactoring: Visual Studio includes code analysis tools for identifying potential issues and code refactoring features to improve code quality and maintainability. Performance Profiling: Developers can apply performance profiling tools to analyze and optimize the performance of their applications. Cross-Platform Development: Visual Studio supports cross-platform development for various platforms, including Windows, macOS, Linux, Android, and iOS. Extensions and Marketplace: Visual Studio can be extended with a vast array of extensions available in the Visual Studio Marketplace, providing additional tools and features. Web Development Tools: Visual Studio provides tools for web development, including support for HTML, CSS, JavaScript, and frameworks like ASP.NET and Node.js. Container Tools: Visual Studio includes tools for working with containers, enabling developers to build and deploy containerized applications. Database Development: Visual Studio includes tools for database development, schema management, and data manipulation, making it suitable for database-centric applications. Game Development: Visual Studio integrates with game development engines like Unity and Unreal Engine, supporting game development workflows. What is the workflow of Visual Studio? The workflow in Visual Studio can vary based on the type of development being undertaken, but here’s a general workflow for a typical software development project: Project Creation: Create a new project in Visual Studio, specifying the project type (e.g., Windows Forms, ASP.NET, Console Application). Coding: Write code using the integrated code editor, taking advantage of features like IntelliSense for code completion and real-time error checking. Building: Build the project to compile the code into executable files or libraries. Visual Studio provides tools for managing build configurations. Debugging: Set breakpoints, run the application in debug mode, and use the integrated debugger to step through code, inspect variables, and identify and fix issues. Version Control: Use Git integration to manage source code versions, branches, and collaborate with team members. Unit Testing: Write and run unit tests to ensure the correctness of the code. Visual Studio provides tools for managing test suites and analyzing test results. Visual Design (Optional): If building a GUI application, use visual designers to create and modify the graphical user interface. Database Development (Optional): If the project involves a database, use Visual Studio’s database tools to manage schema, perform data manipulation, and integrate with data sources. Performance Profiling (Optional): Use performance profiling tools to analyze and optimize the performance of the application, identifying bottlenecks and areas for improvement. Continuous Integration (CI): Integrate with CI/CD (Continuous Integration/Continuous Deployment) systems to automate the build, test, and deployment processes. Deployment: Deploy the application locally or to a server, depending on the project requirements. Documentation: Document the code and project using comments, documentation files, and any relevant documentation tools. Extensions and Marketplace (Optional): Explore and install additional extensions from the Visual Studio Marketplace to enhance the IDE with specific features or tools. Code Review (Optional): Collaborate with team members by conducting code reviews using built-in or external code review tools. Monitoring and Maintenance: Monitor the application’s performance in production, address issues, and perform maintenance tasks as needed. This workflow is adaptable based on the specific needs of different development projects, and developers may leverage additional Visual Studio features and tools depending on the requirements of their applications. How Visual Studio Works & Architecture? How Visual Studio Works & Architecture Visual Studio is an Integrated Development Environment (IDE) developed by Microsoft for building modern applications across various platforms. Here’s a breakdown of how it works and its architecture: 1. Core Components: IDE Shell: Provides the user interface and manages other components. Project System: Manages projects, files, and references. Editor: Allows writing and editing code with features like syntax highlighting, code completion, and refactoring. Compiler and Build Engine: Compiles code into executable files. Debugger: Helps identify and fix errors in your code. Integrated Tools: Provides additional functionality like code analysis, testing tools, and version control integration. 2. Architecture: Layered Architecture: Separates different functionalities into layers for modularity and maintainability. Managed Extensibility Framework (MEF): Allows developers to extend Visual Studio with custom tools and plugins. Windows Presentation Foundation (WPF): Provides the graphical user interface for the IDE. Common Language Runtime (CLR): Executes managed code and provides services like memory management and security. 3. Functionality: Code Editing: Supports various programming languages and provides features like syntax highlighting, code completion, and refactoring. Project Management: Create and manage projects with different configurations and build targets. Debugging: Identify and fix errors in your code with tools like breakpoints, call stacks, and variable inspection. Testing: Integrate various testing frameworks and tools to test your code effectively. Version Control: Integrate with version control systems like Git to manage code changes and collaborate with others. Build Automation: Automate building and deploying applications with features like continuous integration and continuous delivery (CI/CD). Extensible: Extend functionality with plugins and extensions available online. 4. Benefits of Visual Studio Architecture: Modular and Extensible: Allows for flexible customization and adaptation to different needs. Efficient and Scalable: Supports large and complex projects efficiently. Open and Interoperable: Integrates with various tools and technologies. Powerful and Feature-Rich: Provides a comprehensive set of features for developers of all levels. Understanding Visual Studio’s architecture helps developers utilize its full potential and build high-quality applications efficiently. How to Install and Configure Visual Studio? Installing Visual Studio: 1. System Requirements: Ensure your system meets the minimum system requirements for the specific version of Visual Studio you want to install. Download the appropriate installer from the official website 2. Install Options: Individual Components: Choose specific components and workloads based on your needs. Presets: Select pre-configured options for common development scenarios (e.g., Desktop development, Web development). Offline Installation: Download the complete installer package for offline installation on systems without internet access. 3. Installation Process: Follow the on-screen instructions during the installation process. Choose the desired installation location and accept the license terms. Choose the components and workloads you want to install. Wait for the installation process to complete. 4. Configuration: Personalize the IDE: Customize the user interface, keyboard shortcuts, and settings to your preferences. Install extensions: Extend Visual Studio’s functionality with plugins and extensions for specific languages, frameworks, and tools. Configure project settings: Define project properties, build configurations, and debugging options for your development environment. Integrate with additional tools: Connect Visual Studio with other tools you use in your development workflow, such as version control systems and issue tracking systems. By following these steps and utilizing available resources, you can install and configure Visual Studio for your specific needs and start building applications efficiently. Fundamental Tutorials of Visual Studio: Getting started Step by Step Fundamental Tutorials of Visual Studio Following is a Step-by-Step Fundamental Tutorials of Visual Studio: 1. Introduction to Visual Studio: What is Visual Studio? Understand the purpose and functionalities of Visual Studio as an Integrated Development Environment (IDE). Benefits of Visual Studio: Explore the advantages of using Visual Studio, such as its comprehensive features, code editing capabilities, debugging tools, and extensibility. Starting Visual Studio: Learn how to launch the IDE and navigate its basic interface. 2. Exploring the User Interface: Project Explorer: Manage project files and folders. Solution Explorer: Manage solutions containing multiple projects. Code Editor: Write and edit code with syntax highlighting, code completion, and other features. Output Window: View build and debugging output. Toolbar and Menus: Access various features and tools. 3. Creating a New Project: Choose a Project Template: Select a pre-defined template for your desired language and application type (e.g., Console application, Web application). Configure Project Settings: Set project name, location, and other project-specific options. Understanding Project Structure: Explore the generated project files and their roles. 4. Writing and Editing Code: Basic Code Structure: Learn about keywords, variables, data types, operators, and control flow statements. Code Editor Features: Utilize features like syntax highlighting, code completion, and refactoring to write code efficiently. Commenting Your Code: Add comments to explain your code and improve readability. 5. Building and Running Applications: Building: Compile your code into executable files. Debugging: Identify and fix errors in your code using breakpoints, call stacks, and variable inspection. Running: Execute your application and test its functionality. 6. Version Control: Introduction to Version Control: Understand the importance of version control for managing code changes and collaboration. Using Git with Visual Studio: Integrate Git with Visual Studio for version control functionality. Committing and Pushing Changes: Learn how to commit changes to your local repository and push them to a remote repository. 7. Extensions and Customization: Explore the Visual Studio Marketplace: Discover and install extensions to enhance functionality and add new features. Customize the User Interface: personalize the IDE layout, keyboard shortcuts, and settings to fit your workflow. 8. Advanced Topics (Optional): Object-Oriented Programming: Learn about classes, objects, inheritance, and other object-oriented programming concepts. Working with Databases: Connect to databases and interact with data using various frameworks. Web Development: Build web applications using technologies like HTML, CSS, JavaScript, and ASP.NET. Mobile Development: Develop mobile applications for Android and iOS platforms. Tips for Learning Visual Studio: Practice regularly: Start with simple projects and gradually progress to more complex ones. Utilize online resources: Explore tutorials, documentation, and community forums for help and guidance. Join the Visual Studio community: Connect with other developers and learn from their experiences. Experiment and have fun: Explore different features and tools to discover what works best for you. Don’t hesitate to ask for help: There is a vast community of developers willing to assist you on your learning journey. By following these step-by-step tutorials and utilizing available resources, you can gain the fundamental knowledge and skills necessary to use Visual Studio effectively and build amazing applications. The post What is Visual Studio and use cases of Visual Studio? appeared first on DevOpsSchool.com. View the full article

When OpenAI released ChatGPT on November 30, 2022, no one could have anticipated that the following 6 months would usher in a dizzying transformation for human society with the arrival of a new generation of artificial intelligence. Since the emergence of deep learning in the early 2010s, artificial intelligence has entered its third wave of development. The introduction of the Transformer algorithm in 2017 propelled deep learning into the era of large models. OpenAI established the GPT family based on the Decoder part of the Transformer. ChatGPT quickly gained global popularity, astonishing people with its ability to engage in coherent and deep conversations, while also revealing capabilities such as reasoning and logical thinking that reflect intelligence. Alongside the continuous development of AI pre-training with large models, ongoing innovation in Artificial Intelligence Generated Content (Generative AI) algorithms, and the increasing mainstream adoption of multimodal AI, Generative AI technologies represented by ChatGPT accelerated as the latest direction in AI development. This acceleration is driving the next era of significant growth and prosperity in AI, poised to have a profound impact on economic and social development. CEOs may find detailed advice for adopting Gen AI in my recently published article in Harvard Business Review – What CEOs Need to Know About the Costs of Adopting GenAI. Definition and Background of Generative AI Technology Generative AI refers to the production of content through artificial intelligence technology. It involves training models to generate new content that resembles the training data. In contrast to traditional AI, which mainly focuses on recognizing and predicting patterns in existing data, Generative AI emphasizes creating new, creative data. Its key principle lies in learning and understanding the distribution of data, leading to the generation of new data with similar features. This technology finds applications in various domains such as images, text, audio, and video. Among these applications, ChatGPT stands out as a notable example. ChatGPT, a chatbot application developed by OpenAI based on the GPT-3.5 model, gained massive popularity. Within just two months of its release, it garnered over 100 million monthly active users, surpassing the growth rates of all historical consumer internet applications. Generative AI technologies, represented by large language models and image generation models, have become platform-level technologies for the new generation of artificial intelligence, contributing to a leap in value across different industries. The explosion of Generative AI owes much to developments in three AI technology domains: generative algorithms, pre-training models, and multimodal technologies. Generative Algorithms: With the constant innovation in generative algorithms, AI is now capable of generating various types of content, including text, code, images, speech, and more. Generative AI marks a transition from Analytical AI, which focuses on analyzing, judging, and predicting existing data patterns, to Generative AI, which deduces and creates entirely new content based on learned data. Pre-training Models: Pre-training models, or large models, have significantly transformed the capabilities of Generative AI technology. Unlike the past where researchers had to train AI models separately for each task, pre-training large models have generalized Generative AI models and elevated their industrial applications. These large models have strong language understanding and content generation capabilities. Multimodal AI Technology: Multimodal technology enables Generative AI models to generate content across various modalities, such as converting text into images or videos. This enhances the versatility of Generative AI models. Foundational technologies of Generative AI Generative Adversarial Networks (GANs): GANs, introduced in 2014 by Ian Goodfellow and his team, are a form of generative model. They consist of two components: the Generator and the Discriminator. The Generator creates new data, while the Discriminator assesses the similarity between the generated data and real data. Through iterative training, the Generator becomes adept at producing increasingly realistic data. Variational Autoencoders (VAEs): VAEs are a probabilistic generative method. They leverage an Encoder and a Decoder to generate data. The Encoder maps input data to a distribution in a latent space, while the Decoder samples data from this distribution and generates new data. Recurrent Neural Networks (RNNs): RNNs are neural network architectures designed for sequential data processing. They possess memory capabilities to capture temporal information within sequences. In generative AI, RNNs find utility in generating sequences such as text and music. Transformer Models: The Transformer architecture relies on a Self-Attention mechanism and has achieved significant breakthroughs in natural language processing. It’s applicable in generative tasks, such as text generation and machine translation. Applications and Use Cases of Generative AI Text Generation Natural language generation is a key application of Generative AI, capable of producing lifelike natural language text. Generative AI can compose articles, stories, poetry, and more, offering new creative avenues for writers and content creators. Moreover, it can enhance intelligent conversation systems, elevating the interaction experience between users and AI. ChatGPT (short for Chat Generative Pre-trained Transformer) is an AI chatbot developed by OpenAI, introduced in November 2022. It employs a large-scale language model based on the GPT-3.5 architecture and has been trained using reinforcement learning. Currently, ChatGPT engages in text-based interactions and can perform various tasks, including automated text generation, question answering, and summarization. Image Generation Image generation stands as one of the most prevalent applications within Generative AI. Stability AI has unveiled the Stable Diffusion model, significantly reducing the technical barriers for AI-generated art through open-source rapid iteration. Consumers can subscribe to their product DreamStudio to input text prompts and generate artworks. This product has attracted over a million users across 50+ countries worldwide. Audio-Visual Creation and Generation Generative AI finds use in speech synthesis, generating realistic speech. For instance, generative models can create lifelike speech by learning human speech characteristics, suitable for virtual assistants, voice translation, and more. AIGC is also applicable to music generation. Generative AI can compose new music pieces based on given styles and melodies, inspiring musicians with fresh creative ideas. This technology aids musicians in effectively exploring combinations of music styles and elements, suitable for music composition and advertising music. Film and Gaming Generative AI can produce virtual characters, scenes, and animations, enriching creative possibilities in film and game production. Additionally, AI can generate personalized storylines and gaming experiences based on user preferences and behaviors. Scientific Research and Innovation Generative AI can explore new theories and experimental methods in fields like chemistry, biology, and physics, aiding scientists in discovering new knowledge. Additionally, it can accelerate technological innovation and development in domains like drug design and materials science. Code Generation Domain Having been trained on natural language and billions of lines of code, certain generative AI models are proficient in multiple programming languages, including Python, JavaScript, Go, Perl, PHP, Ruby, and more. They can generate corresponding code based on natural language instructions. GitHub Copilot, a collaboration between GitHub and OpenAI, is an AI code generation tool. It provides code suggestions based on naming or contextual code editing. It has been trained on billions of lines of code from publicly available repositories on GitHub, supporting most programming languages. Content Understanding and Analysis Bloomberg recently released a large language model (LLM) named BloombergGPT tailored for the financial sector. Similar to ChatGPT, it employs Transformer models and large-scale pre-training techniques for natural language processing, boasting 500 billion parameters. BloombergGPT’s pre-training dataset mainly comprises news and financial data from Bloomberg, constructing a dataset with 363 billion labels, supporting various financial industry tasks. BloombergGPT aims to enhance users’ understanding and analysis of financial data and news. It generates finance-related natural language text based on user inputs, such as news summaries, market analyses, and investment recommendations. Its applications span financial analysis, investment consulting, asset management, and more. For instance, in asset management, it can predict future stock prices and trading volumes based on historical data and market conditions, providing investment recommendations and decision support for fund managers. In financial news, BloombergGPT automatically generates news summaries and analytical reports based on market data and events, delivering timely and accurate financial information. AI Agents In April 2023, an open-source project named AutoGPT was released on GitHub. As of April 16, 2023, the project has garnered over 70K stars. AutoGPT is powered by GPT-4 and is capable of autonomously achieving any user-defined goals. When presented with a task, AutoGPT autonomously analyzes the problem, proposes an execution plan, and carries it out until the user’s requirements are met. Apart from standalone AI Agents, there’s the possibility of a ‘Virtual AI Society’ composed of multiple AI agents. GenerativeAgents, as explored in a paper titled “GenerativeAgents: Interactive Simulacra of Human Behavior” by Stanford University and Google, successfully constructed a ‘virtual town’ where 25 intelligent agents coexist. Leading business consulting firms predict that by 2030, the generative AI market size will reach $110 billion USD. Operations of Gen AI Operating GenAI involves a comprehensive approach that encompasses the entire lifecycle of GenAI models, from development to deployment and ongoing maintenance. It encompasses various aspects, including data management, model training and optimization, model deployment and monitoring, and continuous improvement. GenAI MLOps is an essential practice for ensuring the success of GenAI projects. By adopting MLOps practices, organizations can improve the reliability, scalability, maintainability, and time-to-market of their GenAI models. Canonical’s MLOps presents a comprehensive open-source solution, seamlessly integrating tools like Charmed Kubeflow, Charmed MLFlow, and Charmed Spark. This approach liberates professionals from grappling with tool compatibility issues, allowing them to concentrate on modeling. Charmed Kubeflow serves as the core of an expanding ecosystem, collaborating with other tools tailored to individual user requirements and validated across diverse platforms, including any CNCF-compliant K8s distribution and various cloud environments. Orchestrated through Juju, an open-source software operator, Charmed Kubeflow facilitates deployment, integration, and lifecycle management of applications at any scale and on any infrastructure. Professionals can selectively deploy necessary components from the bundle, reflecting the composability of Canonical’s MLOps tooling—an essential aspect when implementing machine learning in diverse environments. For instance, while Kubeflow comprises approximately 30 components, deploying just three— Isto, Seldon, and MicroK8s—suffices when operating at the edge due to distinct requirements for edge and scalable operations. View the full article

DevOps Explained: Five Levels of Understanding https://hackernoon.com/devops-explained-five-levels-of-understanding

What is serverless computing? Serverless computing is a cloud computing model that was introduced by AWS in 2014 with its service AWS LAMBDA. The first serverless services were known as Function-as-a-Service (FaaS) but now there are many services such as CaaS or BaaS. It allows developers to build and run applications without the need for managing and maintaining the underlying infrastructure. View the full article

We've recently seen an explosion in the number and the ability of generative artificial intelligence (AI) tools available to everyone, and OpenAI's ChatGPT is leading the way. It hit the 100 million user milestone inside in just two months, and it continues to rapidly grow in terms of its scope and its functionality. In fact, it feels like ChatGPT is having an iPhone moment, changing the technology landscape in fundamental ways that we're only just beginning to understand. It promises to significantly change the way we find and parse information, and the way in which content and art is created. We've put together this ChatGPT explainer to answer all the questions you could possibly have about the AI chatbot, from the origins of the tool to the most recent upgrades in its performance. If you're wondering what ChatGPT is, and what it can do for you, then you're in exactly the right place. You'll have noticed that ChatGPT has sparked something of an AI arms race, with Microsoft building Copilot using similar technology, Google launching its own Google Bard engine, social media apps introducing AI chatbots of their own, and X (formerly Twitter) getting in on the act too. What is ChatGPT? ChatGPT is an AI chatbot that was initially built on a family of Large Language Models (or LLMs), collectively known as GPT-3. OpenAI has now announced that its next-gen GPT-4 models are available. These models can understand and generate human-like answers to text prompts, because they've been trained on huge amounts of data. For example, ChatGPT's most original GPT-3.5 model was trained on 570GB of text data from the internet, which OpenAI says included books, articles, websites, and even social media. Because it's been trained on hundreds of billions of words, ChatGPT can create responses that make it seem like, in its own words, "a friendly and intelligent robot". ChatGPT can answer questions on almost everything (Image credit: ChatGPT) This ability to produce human-like, and frequently accurate, responses to a vast range of questions is why ChatGPT became the fastest-growing app of all time, reaching 100 million users in only two months. The fact that it can also generate essays, articles, and poetry has only added to its appeal (and controversy, in areas like education). But early users have also revealed some of ChatGPT's limitations. OpenAI says that its responses "may be inaccurate, untruthful, and otherwise misleading at times". OpenAI CEO Sam Altman also admitted in December 2022 that the AI chatbot is "incredibly limited" and that "it's a mistake to be relying on it for anything important right now". Still, the world is currently having a ball exploring ChatGPT and, despite the arrival of a paid ChatGPT Plus version for $20 (about £16 / AU$30) a month, you can still use it for free too. What does ChatGPT stand for? ChatGPT stands for "Chat Generative Pre-trained Transformer", which is a bit of a mouthful. Let's take a look at each of those words in turn. The 'chat' naturally refers to the chatbot front-end that OpenAI has built for its GPT language model. The second and third words show that this model was created using 'generative pre-training', which means it's been trained on huge amounts of text data to predict the next word in a given sequence. An illustration from Google's 2017 research paper for the Transformer architecture, which ChatGPT is based on. (Image credit: Google) Lastly, there's the 'transformer' architecture, the type of neural network ChatGPT is based on. Interestingly, this transformer architecture was actually developed by Google researchers in 2017 and is particularly well-suited to natural language processing tasks, like answering questions or generating text. Google was only too keen to point out its role in developing the technology during its announcement of Google Bard. But ChatGPT was the AI chatbot that took the concept mainstream, earning it another multi-billion investment from Microsoft, which said that it was as important as the invention of the PC and the internet. When was ChatGPT released? ChatGPT was released as a "research preview" on November 30, 2022. A blog post casually introduced the AI chatbot to the world, with OpenAI stating that "we’ve trained a model called ChatGPT which interacts in a conversational way". The interface was, as it is now, a simple text box that allowed users to answer follow-up questions. OpenAI said that the dialog format, which you can now see in the Bing search engine and many other places, allows ChatGPT to "admit its mistakes, challenge incorrect premises, and reject inappropriate requests". A paid ChatGPT Plus subscription is available. (Image credit: OpenAI) ChatGPT is based on a language model from the GPT-3.5 series, which OpenAI says finished its training in early 2022. A more advanced GPT-4 model is now available to ChatGPT Plus subscribers. OpenAI did also previously release earlier GPT models in limited form – its GPT-2 language model, for example, was announced in February 2019, but the company said it wouldn't release the fully-trained model "due to our concerns about malicious applications of the technology". OpenAI also released a larger and more capable model, called GPT-3, in June 2020. But it was the full arrival of ChatGPT in November 2022 that saw the technology burst into the mainstream. Throughout the course of 2023, it got several significant updates too, of which more shortly. How much does ChatGPT cost? ChatGPT is still available to use for free, but now also has a paid tier. After growing rumors of a ChatGPT Professional tier, OpenAI said in February that it was introducing a "pilot subscription plan" called ChatGPT Plus in the US. A week later, it made the subscription tier available to the rest of the world. ChatGPT Plus costs $20 p/month (around £16 / AU$30) and brings many benefits over the free tier. It promises to give you full access to ChatGPT even during peak times, which is when you'll otherwise frequently see "ChatGPT is at capacity right now” messages during down times. ChatGPT Plus will cost you $20 a month. (Image credit: OpenAI) OpenAI says the ChatGPT Plus subscribers also get "faster response times", which means you should get answers around three times quicker than the free version (although this is no slouch). And the final benefit is "priority access to new features and improvements", like the experimental 'Turbo' mode that boosts response times even further. It isn't clear how long OpenAI will keep its free ChatGPT tier, but the current signs are promising. The company says "we love our free users and will continue to offer free access to ChatGPT". Right now, the subscription is apparently helping to support free access to ChatGPT. Whether that's something that continues long-term is another matter. It certainly seems that ChatGPT Plus has proved to be a popular option – new sign-ups were briefly paused in November 2023 so that OpenAI could cope with the increase in demand it was seeing. How does ChatGPT work? ChatGPT has been created with one main objective – to predict the next word in a sentence, based on what's typically happened in the gigabytes of text data that it's been trained on. It's sort of like a super-advanced autocorrect. Once you give ChatGPT a question or prompt, it passes through the AI model and the chatbot produces a response based on the information you've given and how that fits into its vast amount of training data. It's during this training that ChatGPT has learned what word, or sequence of words, typically follows the last one in a given context. For a long deep dive into this process, we recommend setting aside a few hours to read this blog post from Stephen Wolfram (creator of the Wolfram Alpha search engine), which goes under the bonnet of 'large language models' like ChatGPT to take a peek at their inner workings. But the short answer? ChatGPT works thanks to a combination of deep learning algorithms, a dash of natural language processing, and a generous dollop of generative pre-training, which all combine to help it produce disarmingly human-like responses to text questions. Even if all it's ultimately been trained to do is fill in the next word, based on its experience of being the world's most voracious reader. What can you use ChatGPT for? ChatGPT has been trained on a vast amount of text covering a huge range of subjects, so its possibilities are nearly endless. But in its early days, users have discovered several particularly useful ways to use the AI helper. Broadly speaking, these can be divided into natural language tasks and coding assistance. In our guide to six exciting ways to use ChatGPT, we showed how you can use it for drafting letters, writing poetry, and creating (or adapting) fiction. That said, it does still have its limitations, as we found when ChatGPT showed us just how far it is from writing a blockbuster movie. That hasn't stopped self-publishing authors from embracing the tech, though. With YouTube and Reddit forums packed with tutorials on how to write a novel using the AI tech, the Amazon Kindle store is already on the cusp of being overrun with ChatGPT-authored books. A ChatGPT plug-in for Google Slides has been developed. (Image credit: MagicSlides) Other language-based tasks that ChatGPT enjoys are translations, helping you learn new languages (watch out, Duolingo), generating job descriptions, and creating meal plans. Just tell it the ingredients you have and the number of people you need to serve, and it'll rustle up some impressive ideas. But ChatGPT is also equally talented at coding and productivity tasks. For the former, its ability to create code from natural speech makes it a powerful ally for both new and experienced coders who either aren't familiar with a particular language or want to troubleshoot existing code. Unfortunately, there is also the potential for it to be misused to create malicious emails and malware. If you look beyond the browser-based chat function to the API, ChatGPT's capabilities become even more exciting. We've learned how to use ChatGPT with Siri and overhaul Apple's voice assistant, which could well stand to threaten the tech giant's once market-leading assistive software. We're also particularly looking forward to seeing it integrated with some of our favorite cloud software and the best productivity tools. There are several ways that ChatGPT could transform Microsoft Office, and someone has already made a nifty ChatGPT plug-in for Google Slides. Microsoft has also announced that the AI tech will be baked into Skype, where it'll be able to produce meeting summaries or make suggestions based on questions that pop up in your group chat. Does ChatGPT have an app? For a while, ChatGPT was only available through its web interface, but there are now official apps for Android and iOS that are free to download. The layout and features are similar to what you'll see on the web, but there are a few differences that you need to know about too. One of the big features you get on mobile that you don't get on the web is the ability to hold a voice conversation with ChatGPT, just as you might with Google Assistant, Siri, or Alexa. Both free and paying users can use this feature in the mobile apps – just tap on the headphones icon next to the text input box. ChatGPT is now available in a mobile app. (Image credit: OpenAI) The arrival of a new ChatGPT API for businesses means we'll soon likely to see an explosion of apps that are built around the AI chatbot. In the pipeline are ChatGPT-powered app features from the likes of Shopify (and its Shop app) and Instacart. The dating app OKCupid has also started dabbling with in-app questions that have been created by OpenAI's chatbot. What is ChatGPT 4? On March 14, OpenAI announced that its next-gen language model, GPT-4, was available to developers and ChatGPT Plus subscribers – with Microsoft confirming that the new Bing is already running on GPT-4. The big change from GPT-3.5 is that OpenAI's newest language model is multimodal, which means it can process both text and images. This means you can show it images and it will respond to them alongside a text prompt – an early example of this, noted by The New York Times, involved giving GPT-4 a photo of some fridge contents and asking what meals you could make from the ingredients. An example surfaced by The New York Times of a GPT-4 model's ability to understand image-based prompts (with mixed results). (Image credit: The New York Times) Apps running on GPT-4, like ChatGPT, have an improved ability to understand context. The model can, for example, produce language that's more accurate and relevant to your prompt or query. GPT-4 is also a better multi-tasker than its predecessor, thanks to an increased capacity to perform several tasks simultaneously. OpenAI also says that safety is a big focus of GPT-4, with OpenAI working for over six months to put it through a better monitoring framework and by alongside experts is a range of specialist fields, like medicine and geopolitics, to make sure its answers are both accurate and sensitive. While GPT-4 isn't a revolutionary leap from GPT-3.5, it is another important step towards chatbots and AI-powered apps that stick closer to the facts and don't go haywire in the ways that we've seen in the recent past. More recently – as well as some chaos at boardroom level – we've seen more upgrades for ChatGPT, particularly for Plus users. You can how get the chatbot to talk and produce images, and pictures can be used as prompts as well. Another new feature is the ability for users to create their own custom bots, called GPTs. For example, you could create one bot to give you cooking advice, and another to generate ideas for your next screenplay, and another to explain complicated scientific concepts to you. View the full article

Artificial Intelligence is a vast subject. In fact, it literally has an infinite amount of sub-subjects and meaningfully related subjects. This article will briefly discuss some of the basics such as Machine Learning, Deep Learning, Artificial Neural Networks, and Algorithms. What Exactly Is Artificial Intelligence (AI)? The primary and often defining goal of Artificial Intelligence is to develop Thinking Machines, primarily computer/software combinations, which can think as well as or better than human beings. These Thinking Machines must have input to think about, the ability to process said input in a prescribed way or algorithms, and desired useful output. We want these Thinking Machines to be intelligent, just as human beings are intelligent. And there’s the rub. What exactly is Human Intelligence? How many types of Human Intelligence are there? If we can’t answer these questions about the nature of Human Intelligence, how can we answer our questions about the nature of Artificial Intelligence? Input, Processing, and Output To address the questions mentioned previously regarding human and Artificial Intelligence, let’s examine some of the human mental functions which are universally accepted as indications of Human Intelligence and to the extent possible, identify corresponding functions of which Thinking Machines are capable. Both Thinking Machines and humans must have input to think about, the ability to process said input in an algorithmic-prescribed way, and the ability to communicate or take action as an outcome of its information processing or output. Both Thinking Machines and humans can fulfill these requirements to a varying extent. Information Input Input comes in the form of Information. To input information to an intelligent entity, be it man or machine, the entity must have the ability to perceive. There are two required components to perception. The first is the ability to sense. Man has five senses: hearing, seeing, smelling, tasting, and touching. As the result of brilliant human work, machines now also have the ability to use the same five senses even though they lack human organs — ears, eyes, nose, tongue, and skin. How this has been accomplished will be the subject of a future Linux Hint AI Essay. The second is the ability to make sense of that which is being sensed. Obviously, humans have, to a certain extent, such an ability. Intelligent Machines, to a certain extent, now also have the same ability. Some examples of machines’ ability to make sense of what they sense include: Image Recognition, Facial Recognition, Speech Recognition, Object Recognition, Pattern Recognition, Handwriting Recognition, Name Recognition, Optical Character Recognition, Symbol Recognition, and Abstract Concept Recognition. Information Processing Again, it is evident that humans can, to a certain extent, process information. We do it all day long, every day. True, sometimes we do a poor job, and at other times we find it impossible to do. But it is fair to say we do it. Now, how about Thinking Machines? Well, they are not entirely unlike humans when it comes to processing information. Sometimes, Thinking Machines do it well, while at other times, they make a mess of it or find it impossible to do. Their failures are not their fault. The fault is ours, as humans. If we provide them with inadequate or inaccurate input, it should be no surprise that their output is unsatisfactory. If we give them a task to do for which we have not prepared them, we can expect them to mess it up or just give up. The Thinking Machines’ failures resulting from humans providing them with bad input deserve little discussion. Garbage in, garbage out. Conversely, preparing our Thinking Machines properly for the tasks we give them to execute is an extremely vast and complex subject. This essay will provide the reader with a rudimentary discussion of the subject. More in-depth discussions of proper input will be covered in forthcoming Linux Hint AI Essays. We have a choice on whether we prepare our Thinking Machines for a single task or an array of complex tasks. The Single Task orientation is known as Weak or Narrow Artificial Intelligence. The Complex Task orientation is known as Strong or General Artificial Intelligence. The advantages and disadvantages of each orientation are readily apparent. The Narrow Intelligence orientation is less costly to program and allows the Thinking Machine to function better at a given task than the General Intelligence oriented machine. The General Intelligence orientation is more expensive to program and enables the Thinking Machine to function on an array of complex tasks. If a Thinking Machine is prepared to process numerous complex aspects of a single subject such as Speech Recognition, it is a hybrid of both Narrow and General Artificial Intelligence. Information Output Artificial Intelligence cannot be considered the equivalent of or even similar to Human Intelligence if it cannot produce the desired useful output. Output can be communicated in any one of numerous forms, including but not limited to written or spoken language, mathematics, graphs, charts, tables, or other formats. Desired useful output can alternatively be in the form of effecting actions. Examples of this include but are not limited to self-driving vehicles and activating and managing the movements of factory machines and robots. Artificial Intelligence Tools The following link will take you to a listing of popular AI Tools. Each Tool is rated for its utility and has a link to the provider’s website. Artificial Intelligence Platforms Artificial Intelligence Platforms simulate the cognitive function that human minds perform, such as problem-solving, learning, reasoning, social intelligence, and general intelligence. Platforms are a combination of hardware and software that allow AI algorithms to run. AI platforms can support the digitalization of data. Some popular AI Platforms include: Azure, Cloud Machine Learning Engine, Watson, ML Platform Services, Leonardo Machine Learning, and Einstein Suite. Artificial Intelligence Is Big Business These are conservative projections, from recognized financial analysts, for World Wide Artificial Intelligence Business Revenues in Billions of US Dollars: Year: 2021 2022 2023 2024 2025 2026 2027 Billions USD 78 110 154 215 301 422 590 Almost all of the leading tech companies are deeply involved in the field of Artificial Intelligence. A few examples: Apple, Google, Facebook, IBM, Nvidia, IBM, Salesforce, Alibaba, Microsoft, and Amazon. The following link will take you to an article that lists the Top 100 AI companies worldwide. For each company, there is a brief description of its AI involvement. https://www.analyticsinsight.net/top-100-artificial-companies-in-the-world/ Machine Learning What Exactly Is Machine Learning? Machine Learning is a subset of Artificial Intelligence. The basic concept is that Thinking Machines can learn to a large extent on their own. Input relevant data or information, and with the use of appropriate algorithms, patterns can be recognized, and the desired useful output can be obtained. As data is inputted and processed, the Machine “learns.” The power and importance of Machine Learning, and its subset Deep Learning, are increasing exponentially due to several factors: The explosion of available utilizable data The rapidly decreasing costs of and increasing ability to store and access Big Data The development and use of increasingly sophisticated algorithms The continuous development of increasingly powerful and less costly computers Types of Machine Learning Algorithms Supervised Learning: The Machine is trained by providing it with both the input and the correct expected output. The Machine learns by comparing its output, which results from its programming, with the accurate output provided. Then, The Machine adjusts its processing accordingly. Learn More About Industries Using This Technology Unsupervised Learning: The Machine is not trained by providing it with the correct output. The Machine must undertake tasks such as pattern recognition, and in effect, it creates its own algorithms. Reinforced Learning: The Machine is provided with algorithms that ascertain, by trial and error, and what works best. Languages for Machine Learning By far, the most popular Language for Machine Learning is Python. Other languages which are less popular but often used are R, Java, JavaScript, Julia, and LISP. Machine Learning Algorithms Another section of this essay discusses Algorithms. Specific Algorithms will be covered in substantial detail in subsequent Linux Hint Essays. Here, we simply list some most often used Machine Learning Algorithms: Linear Regression, Logistic Regression, SVM, Naive Bayes, K-Means, Random Forest, and Decision Tree. Links to Examples of Machine Learning Applications: Rainfall prediction using Linear regression Identifying handwritten digits using Logistic Regression in PyTorch Kaggle Breast Cancer Wisconsin Diagnosis using Logistic Regression Python | Implementation of Movie Recommender System Support Vector Machine to recognize facial features in C++ Decision Trees – Fake (Counterfeit) Coin Puzzle (12 Coin Puzzle) Credit Card Fraud Detection Applying Multinomial Naive Bayes to NLP Problems Image compression using K-means clustering Deep learning | Image Caption Generation using the Avengers EndGames Characters How Does Google Use Machine Learning? How Does NASA Use Machine Learning? 5 Mind-Blowing Ways Facebook Uses Machine Learning Targeted Advertising using Machine Learning How Machine Learning Is Used by Famous Companies? Deep Learning and Neural Networks Key Deep Learning Facts Deep Learning Is Machine Learning on steroids. Deep Learning makes extensive use of Neural Networks to ascertain complicated and subtle patterns in enormous amounts of data. The faster the computers and the more voluminous the data, the better the Deep Learning performance. Deep Learning and Neural Networks can perform automatic feature extraction from raw data. Deep Learning and Neural Networks draw primary conclusions directly from raw data. The primary conclusions are then synthesized into secondary, tertiary, and additional levels of abstraction, as required, to address the processing of large amounts of data and increasingly complex challenges. This data processing and analysis (Deep Learning) is accomplished automatically with extensive use of neural networks and without significant dependence on human input. Within the past decades: 1. Tremendous improvements in computing power and speed, 2. The availability of previously unimaginable volumes of big data, 3. Advances in processing, analysis, and storage of data, and 4. The use of the cloud and the internet — Deep Learning at today’s performance levels would be virtually impossible. Deep Neural Networks IQ Can Equal and Exceed Human’s Highest IQs Deep Neural Networks have multiple levels of processing nodes. As the levels of nodes increase, the cumulative effect is the Thinking Machines’ increasing capability of formulating abstract representations. All IQ tests are essentially methods of measuring a person’s capability for abstract reasoning. Deep Learning utilizes multiple levels of representation achieved by organizing non-linear information into representations at a given level which in turn is transformed into more abstract representations at the next highest level. The higher levels are not designed by humans but are learned by the Thinking Machines from data processed at lower levels. Deep Learning vs. Machine Learning To detect money laundering or fraud, Traditional Machine Learning might rely on a single factor such as the dollar amounts and frequency of a person’s transactions, while Deep Learning might include additional factors such as times, locations, and IP address. The first level of Neural Networking fraud detection might focus on a single factor of raw data such as the dollar amounts of transactions. The first level of analysis is then passed on to the second level of processing which might focus on user IP addresses. The product of the second level of processing can then be passed on to a higher level which focuses on an additional potential fraud indication and so on. This process allows the machine to learn as it proceeds and improves the machine’s pattern recognition until a final result is obtained. We use the term Deep Learning because Neural Networks can have numerous deep levels that enhance learning. Examples of How Deep Learning Is Utilized Online Virtual Assistants like Alexa, Siri, and Cortana use Deep Learning to understand human speech. Deep Learning algorithms automatically translate between languages. Deep Learning enables, among many other things, the development of driverless delivery trucks, drones, and autonomous cars. Deep Learning enables Chatbots and ServiceBots to respond to auditory and text questions intelligently. Facial Recognition by machines is impossible without Deep Learning. Pharmaceutical companies are using Deep Learning for drug discovery and development. Physicians are using Deep Learning for disease diagnosis and the development of treatment regimes. Algorithms What Are Algorithms? An Algorithm is a process — a set of step-by-step rules to be followed in calculations or for other problem-solving methods. Types of Algorithms Algorithm types include but are hardly limited to the following: Simple recursive algorithms Backtracking algorithms Divide and conquer algorithms Dynamic programming algorithms Greedy algorithms Branch and bound algorithms Brute force algorithms Randomized algorithms Training Neural Networks Neural Networks must be trained using algorithms. Algorithms used to train Neural Networks include but are in no way limited to the following: Gradient descent, Newton’s method, Conjugate gradient, Quasi-Newton method, and Levenberg-Marquardt. Computation Complexity of Algorithms The computational complexity of an algorithm is a measure of the amount of resources that use a given algorithm required. Mathematical measures of complexity are available, which can predict how fast it will run, and how much computing power and memory it will require prior to the employment of an algorithm. Usage of some algorithms requires more than others. In some cases, the required complexity of an indicated algorithm might be so extensive that it becomes impractical to employ. Thus, a heuristic algorithm, which produces approximate results, may be utilized. Conclusion Artificial intelligence in concept, has vast aspirations. In practice, the applications are more humble and in use today. Machine learning is a simple step forward in Artificial Intelligence, but only the future will unlock the full potential of this technology. View the full article