brief introduction
This book is a comprehensive guide to learning machine learning and deep learning in the PyTorch environment. It can be used as an introductory tutorial for beginners, or as a reference book for readers when developing machine learning projects.
With clear explanations and vivid examples, this book provides an in-depth introduction to the basics of machine learning methods. It not only provides instructions for building machine learning models, but also provides basic guidelines for building machine learning models and solving practical problems. This book adds PyTorch-based deep learning content and introduces the new version of Scikit-Learn. This book covers a variety of machine learning and deep learning methods for text and image classification, introducing generative adversarial networks (GANs) for generating new data and reinforcement learning for training agents. Finally, the book also introduces new dynamics in deep learning, including graph neural networks and large transformers for natural language processing (NLP). Whether you are new to machine learning or plan to track the progress of machine learning, this book can be used as the best choice for machine learning with Python.
About the Author
Sebastian Raschka
received his Ph.D. from Michigan State University and is now an assistant professor of statistics at the University of Wisconsin-Madison, conducting research in machine learning and deep learning. His research interests are data-constrained few-shot learning and building deep neural networks for predicting ordered target values. He is also an open source contributor as Grid.ai's Chief AI Educator, passionate about disseminating machine learning and AI domain knowledge.
Liu Yuxi (Hayden) [Yuxi (Hayden) Liu]
works as a machine learning software engineer at Google and has worked as a machine learning scientist. He is the author of a series of books on machine learning. His first book, Python Machine Learning By Example, was ranked #1 in its category on Amazon in 2017 and 2018 and has been translated into several languages.
Vahid Mirjalili (Vahid Mirjalili)
received a double Ph.D. in Mechanical Engineering and Computer Science from Michigan State University. He is a researcher focusing on computer vision and deep learning.
Author Sebastian Raschka is great at explaining complex methods and concepts in an easy-to-understand manner. As the deep learning revolution penetrated into various fields, Sebastian Raschka and his team continued to upgrade and improve the content of the book, and successively published the second and third editions. Based on the previous three editions, this book has added some new chapters, including PyTorch-related content, covering Transformer and graph neural networks. These are the current state-of-the-art methods in the field of deep learning, sweeping fields such as text understanding and molecular structure by storm in the past two years.
The authors' expertise and experience in solving real-world problems provide an excellent balance of theoretical knowledge and hands-on practical content. Sebastian Raschka and Vahid Mirjalili have extensive scientific research experience in the fields of computer vision and computational biology. Yuxi Liu is good at solving practical problems in the field of machine learning, such as applying machine learning methods to event prediction, recommendation systems, etc. The authors of this book are all passionate about education, and they have written this book in easy-to-understand language to meet the needs of readers.
learning background
In recent years, machine learning methods have been widely used in industries such as healthcare, robotics, biology, physics, mass consumption, and Internet services due to their ability to understand massive amounts of data and make autonomous decisions. Since the AlexNet model was proposed in the ImageNet competition in 2012, machine learning and deep learning have developed rapidly, achieving milestones one after another, profoundly affecting industry, academia and people's lives.
Today, machine learning, deep learning, and artificial intelligence have become the most popular research directions in the information field, and jobs in these fields in the job market are also very attractive. Giant leaps in science often come from brilliant ideas and easy-to-use tools, and machine learning is no exception.
Applying machine learning in practice requires a combination of theory and tools. For introductory readers of machine learning, it is difficult to understand the principles and concepts to determine the software packages to be installed. When many people first try machine learning, they will find it really difficult to understand what an algorithm is doing. Not only because of various complicated mathematical theories and difficult symbols in the algorithm, but without practical examples, it is really boring to understand an algorithm by definition and derivation. Even in the relevant guidance materials on the Internet, what can be found are usually various formulas and obscure explanations, and few people can explain all the details in detail.
Therefore, the positioning of the book "Python Machine Learning: Based on PyTorch and Scikit-Learn" is to combine machine learning theory with engineering practice, thereby lowering the reading threshold for readers . From the fundamentals of data-driven methods to the latest deep learning frameworks, each chapter of this book provides machine learning code examples for solving practical machine learning problems.
"Python Machine Learning: Based on PyTorch and Scikit-Learn"
[US] Sebastian Laschka, [US] Liu Yuxi (Hayden),
[US] Wahid Mirjalili
Dmytro Dzhulgakov , one of the "Four Great Masterpieces" of Python Deep Learning, the new PyTorch version
of PyTorch core maintainer
Personally recommended, this book not only introduces the basic principles of the field of machine learning, but also introduces the engineering practice of machine learning. The value of this book is immeasurable, and I hope that this book will inspire readers to use machine learning for their own research fields.
After finishing this book, you will be able to:
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Explore frameworks, models, and methods for machines to "learn" from data.
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Implement machine learning with Scikit-Learn and deep learning with PyTorch.
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Train a machine learning classifier to classify data such as images, text, and more.
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Build and train neural networks, transformers, and graph neural networks.
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Discover the best ways to evaluate and optimize models.
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Use regression analysis to predict continuous target outcomes.
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Dig deep into text and social media data using sentiment analysis.
Recommended book: Python Machine Learning
Aic Squirrel Activity: This is the end of the recommended high-quality books in this issue, see you in the next issue!
Deadline: 7.20
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