Machine Learning with PyTorch and Scikit-Learn

Sebastian Raschka is an Assistant Professor of Statistics at the University of Wisconsin-Madison, where he studies machine learning and deep learning. Sebastian hopes to continue helping people learn about machine learning and artificial intelligence as the Lead AI Educator at Grid AI.

Yuxi (Hayden) Liu works at Google as a Machine Learning Software Engineer. He is creating and upgrading machine learning models and systems for ad optimization on the world's largest search engine.

Vahid Mirjalili works as a deep learning researcher on CV apps. Michigan State University awarded Vahid a Ph.D. in both Mechanical Engineering and Computer Science.

Machine Learning with PyTorch and Scikit-Learn is a comprehensive reference to using PyTorch for machine learning and deep learning. It serves as both a step-by-step instruction and a reference that you will return to as you create your machine learning systems.

The book covers all of the essential machine learning techniques in full, with clear explanations, visuals, and examples. While some books merely teach you to follow instructions, this machine learning book teaches you the ideas that will allow you to construct models and applications for yourself.

PyTorch is a Pythonic approach to machine learning that makes it easy to learn and code with. This book discusses the fundamentals of PyTorch and how to build models with popular libraries like PyTorch Lightning and PyTorch Geometric.

You will also learn about generative adversarial networks (GANs) for generating new data and using reinforcement learning to train intelligent agents. Finally, this new version has been expanded to include the most recent deep learning trends, such as graph neural networks and large-scale transformers for natural language processing (NLP).

This PyTorch book is your machine learning buddy, whether you're a Python developer new to machine learning or want to brush up on the latest breakthroughs.

What you will discover:

• Investigate frameworks, models, and strategies for machine learning from data.
• For machine learning, use scikit-learn, and for deep learning, use PyTorch.
• Train machine learning classifiers on photos, text, and other data sources.
• Create and test neural networks, transformers, and boosting algorithms.
• Learn about the best procedures for reviewing and tuning models.
• Using regression analysis, predict continuous target outcomes.
• Using sentiment analysis, delve deeper into textual and social media data.

If you know the fundamentals of Python and want to learn about machine learning and deep learning, this is the book for you. This is a must-have resource for developers and data scientists who wish to use scikit-learn and PyTorch to build practical machine learning and deep learning applications. Before you begin reading this book, you should be familiar with calculus and linear algebra.

Author: Sebastian Raschka, Yuxi Liu and Vahid Mirjalili

Link to buy: https://www.amazon.com/Machine-Learning-PyTorch-Scikit-Learn-learning/dp/1801819319/

Ratings: 4.8 out of 5 stars (from 109 reviews)

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