Pythonic AI
eBook - ePub

Pythonic AI

A beginner's guide to building AI applications in Python (English Edition)

  1. English
  2. ePUB (mobile friendly)
  3. Available on iOS & Android
eBook - ePub

Pythonic AI

A beginner's guide to building AI applications in Python (English Edition)

About this book

Unlock the power of AI with Python: Your Journey from Novice to Neural Nets

Key Features
? Learn to code in Python and use Google Colab's hardware accelerators (GPU and TPU) to train and deploy AI models efficiently.
? Develop Convolutional Neural Networks (CNNs) using the TensorFlow 2 library for computer vision tasks.
? Develop sequence, attention-based, and Transformer models using the TensorFlow 2 library for Natural Language Processing (NLP) tasks.

Description
"Pythonic AI" is a book that teaches you how to build AI models using Python. It also includes practical projects in different domains so you can see how AI is used in the real world. Besides teaching how to build AI models, the book also teaches how to understand and explore the opportunities that AI presents. It includes several hands-on projects that walk you through successful AI applications, explaining concepts like neural networks, computer vision, natural language processing (NLP), and generative models. Each project in the book also reiterates and reinforces the important aspects of Python scripting. You'll learn Python coding and how it can be used to build cutting-edge AI applications. The author explains each essential line of Python code in detail, taking into account the importance and difficulty of understanding. By the end of the book, you will learn how to develop a portfolio of AI projects that will help you land your dream job in AI.

What you will learn
? Create neural network models using the TensorFlow 2 library.
? Develop Convolutional Neural Networks (CNNs) for computer vision tasks.
? Develop Sequence models for Natural Language Processing (NLP) tasks.
? Create Attention-based and Transformer models.
? Learn how to create Generative Adversarial Networks (GANs).

Who this book is for
This book is for everyone who wants to learn how to build AI applications in Python, regardless of their experience level. Whether you're a student, a tech professional, a non-techie, or a technology enthusiast, this book will teach you the fundamentals of Python and AI, and show you how to apply them to real-world problems.

Table of Contents
1. Python Kickstart: Concepts, Libraries, and Coding
2. Setting up AI Lab
3. Design My First Neural Network Model
4. Explore Designing CNN with TensorFlow
5. Develop CNN-based Image Classifier Apps
6. Train and Deploy Object Detection Models
7. Create a Text and Image Reader
8. Explore NLP for Advanced Text Analysis
9. Up and Running with Sequence Models
10. Using Sequence Models for Automated Text Classification
11. Create Attention and Transformer Models
12. Generating Captions for Images
13. Learn to Build GAN Models
14. Generate Artificial Faces Using GAN

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Yes, you can access Pythonic AI by Arindam Banerjee in PDF and/or ePUB format, as well as other popular books in Computer Science & Artificial Intelligence (AI) & Semantics. We have over one million books available in our catalogue for you to explore.

Table of contents

  1. Cover Page
  2. Title Page
  3. Copyright Page
  4. Dedication
  5. About the Author
  6. About the Reviewer
  7. Acknowledgement
  8. Preface
  9. Table of Contents
  10. 1. Python Kickstart: Concepts, Libraries, and Coding
  11. 2. Setting up AI Lab
  12. 3. Design My First Neural Network Model
  13. 4. Explore Designing CNN with TensorFlow
  14. 5. Develop CNN-based Image Classifier Apps
  15. 6. Train and Deploy Object Detection Models
  16. 7. Create a Text and Image Reader
  17. 8. Explore NLP for Advanced Text Analysis
  18. 9. Up and Running with Sequence Models
  19. 10. Using Sequence Models for Automated Text Classification
  20. 11. Create Attention and Transformer Models
  21. 12. Generating Captions for Images
  22. 13. Learn to Build GAN Models
  23. 14. Generate Artificial Faces Using GAN
  24. Index