Dive into computer vision, natural language processing, and recommender systems by building end-to-end projects in PyTorch — one of the most widely used deep learning frameworks among researchers and engineers worldwide. This book takes you from the fundamentals to complete, hands-on projects, giving you the confidence to start creating your own AI solutions.

The book begins with a chapter on the fundamentals of machine learning, laying the groundwork by introducing key aspects of an ML project such as data preprocessing, feature engineering, model training, and evaluation, along with essential concepts like overfitting and underfitting. The following chapter, "Tensors in PyTorch," explores data handling in PyTorch -- from basic tensor operations to advanced gradient computations -- providing a deeper understanding of data transformations.

With the foundations in place, the book moves on to hands-on projects. Chapter 3 introduces you to the world of computer vision, where you will build an image classifier using convolutional neural networks. The next three chapters immerse you in natural language processing: beginning with text classification (Chapter 4), tackling a range of NLP tasks with Hugging Face (Chapter 5), and culminating in the creation of a storytelling language model (Chapter 6).

The focus then shifts to other key AI domains – you will tackle an audio classification task (Chapter 7), build a recommender system in PyTorch (Chapter 8), and finish with a multi-modal project that combines computer vision and natural language processing to build an image captioning system (Chapter 9).

Whether you're a software engineer looking to break into the world of AI or a beginner with basic Python skills, "AI Projects with PyTorch" offers practical guidance and hands-on experience to start building your own AI applications with confidence.

What you will learn:

Master the core principles of machine learning and gain confidence with the typical PyTorch project workflow.

Build a solid understanding of data handling in PyTorch – including tensors, datasets, data loaders, and gradient computations.

Build natural language processing models, from text classification to storytelling language models.

Work on multiple natural language processing tasks with Hugging Face libraries.

Combine vision and language to build an image captioning system.

Who this is book is for:

- Python programmers and software engineers who are new to AI and want a practical, project-based introduction with PyTorch.

ML engineers who wish to expand into other AI domains such as computer vision, natural language processing, audio processing, etc.

Table of Contents

Chapter 1: Introduction to Machine Learning

Chapter 2: Tensors in PyTorch

Chapter 3: Image Classification Using Convolutional Neural Networks

Chapter 4: Introduction to Natural Language Processing: Building a Text Classifier

Chapter 5: Practical Natural Language Processing with Hugging Face

Chapter 6: Building a Language Model for Storytelling

Chapter 7: Audio Classification with PyTorch

Chapter 8: Recommender Systems with PyTorch

Chapter 9: Image Captioning with PyTorch

About the Author

Siddhesh Prashant Chaubal has dedicated his career to building and studying intelligent systems — from cutting-edge research in artificial intelligence to large-scale machine learning platforms powering real-world applications. Currently, he works as a Research Scientist at Dream11 in Mumbai. In earlier roles, he has served as a Staff Engineer at Qualcomm India and an applied scientist at Amazon in Seattle. He holds a B.Tech. in Computer Science from IIT Bombay and a PhD from the University of Texas at Austin, where his research explored theoretical aspects of computer science and machine learning. His work has been published in leading international conferences such as CIKM and MFCS. When not immersed in AI, he enjoys reading, playing chess, or listening to music.