Pythonic AI

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.

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 the 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