Download
Initially, this project started as the 4th edition of Python Machine Learning. However, after putting so much passion and hard work into the changes and new topics, we thought it deserved a new title. So, what’s new? There are many contents and additions, including the switch from TensorFlow to PyTorch, new chapters on graph neural networks and transformers, a new section on gradient boosting, and many more that I will detail in a separate blog post. For those who are interested in knowing what this book covers in general, I’d describe it as a comprehensive resource on the fundamental concepts of machine learning and deep learning. The first half of the book introduces readers to machine learning using scikit-learn, the defacto approach for working with tabular datasets. Then, the second half of this book focuses on deep learning, including applications to natural language processing and computer vision.
Features
- Basic knowledge of Python is required
- This book will take readers on a journey from understanding machine learning from the ground up
- Train advanced deep learning models by the end of the book
- We added a new chapter on Generative Adversarial Networks
- Comprehensive introduction to reinforcement learning based on numerous requests from readers
- book’s GitHub repository with code examples, table of contents, and additional information
Project Samples
