Tools and Technologies for AI Development

The chapter delves into the essential tools and technologies necessary for AI development, highlighting key Python libraries, interactive development environments, and outlining the AI development lifecycle. It emphasizes the significance of structured workflows in building AI systems to ensure effective outcomes. By leveraging powerful libraries such as TensorFlow, PyTorch, and Scikit-learn, along with platforms like Jupyter Notebooks and Google Colab, practitioners can accelerate AI innovation and deployment.

Sections

  • 14

    Tools And Technologies For Ai Development

    This chapter explores essential tools and technologies needed for efficient AI development, including libraries and development environments.

    Learning Practice

  • 14.1

    Introduction

    This section introduces the essential tools and technologies required for developing AI systems, emphasizing libraries and best practices.

    Learning Practice

  • 14.2

    Python Libraries For Ai

    This section covers the key Python libraries majorly used in AI development, highlighting TensorFlow, PyTorch, and Scikit-learn.

    Learning Practice

  • 14.2.1

    Tensorflow

    TensorFlow is an open-source library developed by Google for machine learning and deep learning, providing tools for building and deploying neural networks.

    Learning Practice

  • 14.2.2

    Pytorch

    PyTorch is a dynamic and user-friendly deep learning library developed by Facebook's AI Research lab, favored for research and rapid prototyping.

    Learning Practice

  • 14.2.3

    Scikit-Learn

    Scikit-learn is a user-friendly Python library tailored for traditional machine learning algorithms.

    Learning Practice

  • 14.3

    Interactive Development Environments

    Interactive development environments like Jupyter Notebooks and Google Colab facilitate hands-on coding and experimentation in AI development.

    Learning Practice

  • 14.3.1

    Jupyter Notebooks

    Jupyter Notebooks is an interactive web application that enables users to write live code, visualize data, and combine descriptive text, largely used in data analysis and AI prototyping.

    Learning Practice

  • 14.3.2

    Google Colab

    Google Colab is a cloud-based Jupyter Notebook environment that offers free access to GPUs and TPUs, facilitating collaboration and sharing.

    Learning Practice

  • 14.4

    Ai Development Lifecycle

    The AI Development Lifecycle outlines the structured steps required to develop effective AI systems.

    Learning Practice

References

Chapter 14_ Tools and Technologies for AI Development.pdf

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What we have learnt

  • Python is the most widely u...
  • Key libraries such as Tenso...
  • The AI development lifecycl...

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