Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python (Paperback)

Machine Learning with PyTorch and Scikit-Learn is a comprehensive guide to machine learning and deep learning with PyTorch. It acts as both a step-by-step tutorial, and a reference you'll keep coming back to as you build your machine learning systems.

Packed with clear explanations, visualizations, and examples, this book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, we teach you the principles to build models and applications for yourself.

Updated to cover deep learning using PyTorch, this book also introduces readers to the latest additions to scikit-learn. Moreover, this book covers various machine learning and deep learning techniques for text and image classification. You will also learn about generative adversarial networks (GANs) for generating new data and training intelligent agents with reinforcement learning. Finally, this new edition is also expanded to cover the latest trends in deep learning, including introductions to graph neural networks and large-scale transformers used for natural language processing (NLP).

This PyTorch book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments.

Sebastian Raschka

Sebastian Raschka is an Assistant Professor of Statistics at the University of Wisconsin-Madison focusing on machine learning and deep learning research. As Lead AI Educator at Grid.ai, Sebastian plans to continue following his passion for helping people get into machine learning and artificial intelligence.

Yuxi (Hayden) Liu

Yuxi (Hayden) Liu is a Software Engineer, Machine Learning at Google. He is developing and improving machine learning models and systems for ads optimization on the largest search engine in the world.

Vahid Mirjalili

Vahid Mirjalili is a deep learning researcher focusing on CV applications. Vahid received a Ph.D. degree in both Mechanical Engineering and Computer Science from Michigan State University.