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

I. THÔNG TIN SẢN PHẨM
Mã sản phẩm : STT610
Nhà xuất bản:  Packt Publishing (February 25, 2022)
Tác giả :   Sebastian Raschka, Yuxi (Hayden) Liu, Vahid Mirjalili
Ngôn Ngữ : Tiếng Anh
ISBN      : 1801819319
Số trang    : 770 pages
Hình thức : Bìa Mềm, IN ĐEN TRẮNG
Loại : Sách gia công đóng gáy keo chắc chắn chất lượng cao
Giấy in : Giấy ngoại định lượng 70msg, viết vẽ và hightlight thoải mái.
Chất lượng : Bản in rõ nét, giá rất tốt cho mọi người.

II. MÔ TẢ SẢN PHẨM
1.Mô tả sản phẩm
This book of the bestselling and widely acclaimed Python Machine Learning series is a comprehensive guide to machine and deep learning using PyTorch's simple to code framework

Key Features
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
 

Book Description
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, 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 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.

What you will learn

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

Table of Contents

• Giving Computers the Ability to Learn from Data
• Training Simple Machine Learning Algorithms for Classification
• A Tour of Machine Learning Classifiers Using Scikit-Learn
• Building Good Training Datasets – Data Preprocessing
• Compressing Data via Dimensionality Reduction
• Learning Best Practices for Model Evaluation and Hyperparameter Tuning
• Combining Different Models for Ensemble Learning
• Applying Machine Learning to Sentiment Analysis
• Predicting Continuous Target Variables with Regression Analysis
• Working with Unlabeled Data – Clustering Analysis
• (N.B. Please use the Look Inside option to see further chapters)

        
2. Tác giả
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 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 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.