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

"Machine Learning with PyTorch and Scikit-Learn" is a comprehensive guide designed for developers and data scientists seeking to master both machine learning and deep learning techniques. As part of a bestselling series, this updated edition shifts its focus to PyTorch, offering a "Pythonic" approach that simplifies coding while providing a robust theoretical foundation. The book serves as both a step-by-step tutorial and a long-term reference, moving beyond simple instructions to teach the core principles required to build independent models and applications. Readers are expected to have a grasp of Python basics, calculus, and linear algebra before diving into the material. The content covers a wide array of essential topics, starting with fundamental machine learning algorithms using scikit-learn and progressing to advanced deep learning architectures with PyTorch. Key areas include training classifiers on images and text, regression analysis, and data preprocessing. This edition has been significantly expanded to address modern trends, featuring in-depth explorations of graph neural networks, large-scale transformers for natural language processing (NLP), and Generative Adversarial Networks (GANs). It also introduces practical tools like PyTorch Lightning and PyTorch Geometric to streamline model creation. Beyond specific algorithms, the text emphasizes best practices for model evaluation, hyperparameter tuning, and ensemble learning. Readers will learn to implement multilayer artificial neural networks from scratch and explore dynamic computation graphs and automatic differentiation. By combining clear visualizations with intuitive explanations, the authors aim to equip readers with the skills to predict continuous target outcomes, analyze sentiment in social media data, and train intelligent agents using reinforcement learning. This resource ultimately bridges the gap between theoretical concepts and practical, real-world application.