Hands-On Machine Learning with Scikit-Learn and PyTorch (Early Release)КНИГИ » ПРОГРАММИНГ
Название: Hands-On Machine Learning with Scikit-Learn and PyTorch: Concepts, Tools, and Techniques to Build Intelligent Systems (Early Release) Автор: Aurélien Geron Издательство: O’Reilly Media, Inc. Год: 2025-05-15 Страниц: 399 Язык: английский Формат: epub (true) Размер: 39.96 MB
The potential of Machine Learning today is extraordinary, yet many aspiring developers and tech professionals find themselves daunted by its complexity. Perhaps you're ready to jump in, but you're unsure where or how to begin. Whether you're looking to enhance your skill set and apply machine learning to real-world projects or are simply curious about how AI systems function, this book is your jumping-off place.
In a way that's approachable yet deeply informative, author Aurelien Geron delivers the ultimate introductory guide to Machine Learning and Deep Learning. With a focus on clear explanations and real-world Python examples, the book takes you through cutting-edge tools like Scikit-learn and PyTorch—from basic regression techniques to advanced neural networks like transformers and generative adversarial networks. Whether you're a student, professional, or hobbyist, you'll gain the skills to begin building intelligent systems.
Understand ML basics, including concepts like overfitting and hyperparameter tuning Learn to build end-to-end ML projects using scikit-learn, from data exploration to model evaluation Explore advanced architectures like convolutional and recurrent neural networks with PyTorch Discover techniques for unsupervised learning, such as clustering and anomaly detection Increase your expertise in state-of-the-art AI systems by fine-tuning pretrained models Build tangible skills with complete hands-on coding exercises and real-world applications
Prerequisites: This book assumes that you have some Python programming experience. This book also assumes that you are familiar with Python’s main scientific libraries—in particular, NumPy, Pandas, and Matplotlib. If you have never used these libraries, don’t worry; they’re easy to learn, and I’ve created a tutorial for each of them. Moreover, if you want to fully understand how the Machine Learning algorithms work (not just how to use them), then you should have at least a basic understanding of a few math concepts, especially linear algebra. Specifically, you should know what vectors and matrices are, and how to perform some simple operations like adding vectors, or transposing and multiplying matrices. You will also find a tutorial on differential calculus, which may be helpful to understand how neural networks are trained, but it’s not entirely essential to grasp the important concepts. This book also uses other mathematical concepts occasionally, such as exponentials and logarithms, a bit of probability theory, and some basic concepts from statistics, but nothing too advanced.
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