Machine Learning With Pytorch And Scikitlearn 1st Edition Sebastian Raschka by Sebastian Raschka, Yuxi (hayden) Liu, Dr. Vahid Mirjalili 9781801816380, 1801816387 instant download after payment.

• Learn applied machine learning with a solid foundation in theory
• Clear, intuitive explanations take you deep into the theory and practice of Python machine learning
• Fully updated and expanded to cover PyTorch, transformers, XGBoost, graph neural networks, and best practices

ML 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, the 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 the principles allowing you to build models and applications for yourself.

Why PyTorch?

PyTorch is the Pythonic way to learn machine learning, making it easier to learn and simpler to code with. This book explains the essential parts of PyTorch and how to create models using popular libraries, such as PyTorch Lightning and PyTorch Geometric.

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 expanded to cover the latest trends in deep learning, including graph neural networks and large-scale transformers used for 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.

• Explore frameworks, models, and techniques for machines to 'learn' from data
• Use scikit-learn for machine learning and PyTorch for DL
• Train ML classifiers on images, text, and more
• Build and train neural networks, transformers, and boosting algorithms
• Discover best practices for evaluating and tuning
• Predict continuous target outcomes using regression analysis