Лучшие книги о машинном обучении

http://www.deeplearningbook.org/ .. This book can be useful for a variety of readers, but we wrote it with two main target audiences in mind. One of these target audiences is university students (undergraduate or graduate) learning about machine learning, including those who are beginning a career in deep learning and artificial intelligence research. The other target audience is software engineers who do not have a machine learning or statistics background, but want to rapidly acquire one and begin using deep learning in their product or platform. Deep learning has already proven useful in many software disciplines including computer vision, speech and audio processing, natural language processing, robotics, bioinformatics and chemistry, video games, search engines, online advertising and finance. This book has been organized into three parts in order to best accommodate a variety of readers. Part I introduces basic mathematical tools and machine learning concepts. Part II describes the most established deep learning algorithms that are essentially solved technologies. Part III describes more speculative ideas that are widely believed to be important for future research in deep learning. We do assume that all readers come from a computer science background. We assume familiarity with programming, a basic understanding of computational performance issues, complexity theory, introductory level calculus and some of the terminology of graph theory.

This practical book is perfect for programmers interested in delving into the exciting field of machine learning. With concrete examples, minimal theory, and Python frameworks like Scikit-Learn and TensorFlow, the author shows readers how to build intelligent systems capable of learning from data. From simple linear regression to deep neural networks, you'll gain an intuitive understanding of machine learning techniques and architectures while getting hands-on experience through exercises in each chapter. Dive into the machine learning landscape and start building intelligent systems today.

This machine learning book covers everything modern machine learning has to offer and can be read in just one week. It's concise and up to date, written by an experienced practitioner. Plus, it has a continuously updated wiki with additional resources. With flexible pricing and formats, you can choose what suits you best, and you can even read the book chapters for free before deciding whether to buy.

"Discover the cutting-edge world of pattern recognition and machine learning with this comprehensive textbook. Bayesian methods and graphical models have transformed these fields in the past decade, and this book explores them while providing a thorough introduction to the subject matter. Perfect for advanced students, researchers, and practitioners, this book assumes no prior knowledge of the concepts and includes a self-contained introduction to basic probability theory. Get ready to dive into the exciting world of pattern recognition and machine learning!"

Learn the practical components and statistical concepts of machine learning with this clear and concise guide for absolute beginners. Written without the need for programming experience, you'll be introduced to core algorithms and visual examples to guide you through creating your first machine learning model using Python. The second edition includes new topics such as data scrubbing and ensemble modeling, making it an excellent starting point for those ready to step into the world of machine learning. This book is not a sequel and is a revamped version of the first edition, but with additional information.

Ныне машинное обучение стало неотъемлемой частью различных коммерческих и исследовательских проектов, и не следует думать, что эта область - прерогатива исключительно крупных компаний с мощными командами аналитиков. Эта книга научит вас практическим способам построения систем МО, даже если вы еще новичок в этой области. В ней подробно объясняются все этапы, необходимые для создания успешного проекта машинного обучения, с использованием языка Python и библиотек scikit-learn, NumPy и matplotlib. Авторы сосредоточили свое внимание исключительно на практических аспектах применения алгоритмов машинного обучения, оставив за рамками книги их математическое обоснование. Данная книга адресована специалистам, решающим реальные задачи, а поскольку область применения методов МО практически безгранична, прочитав эту книгу, вы сможете собственными силами построить действующую систему машинного обучения в любой научной или коммерческой сфере.

This comprehensive textbook introduces readers to machine learning and how it can be used to automatically detect patterns in data and predict future data. The author uses a unified, probabilistic approach and covers important background topics such as probability, optimization, and linear algebra. Recent developments in the field are discussed, and the book is filled with color images and examples from biology, text processing, computer vision, and robotics. The book stresses a model-based approach and includes pseudo-code for important algorithms. Suitable for upper-level undergraduates and beginning graduate students.

Глубокое обучение - Deep learning - это набор алгоритмов машинного обучения, которые моделируют высокоуровневые абстракции в данных, используя архитектуры, состоящие из множества нелинейных преобразований. Согласитесь, эта фраза звучит угрожающе. Но всё не так страшно, если о глубоком обучении рассказывает Франсуа Шолле, который создал Keras - самую мощную библиотеку для работы с нейронными сетями.

Discover the world of data mining and machine learning with this comprehensive guide. Written by three prominent professors of statistics, this book provides a common conceptual framework for understanding the tools and ideas in various fields such as medicine, biology, finance, and marketing. With a focus on concepts rather than mathematics, it covers a broad range of topics including neural networks, support vector machines, classification trees, and boosting. With many examples and color graphics, this is a valuable resource for statisticians and anyone interested in data mining in science or industry.

Discover the exciting world of machine learning and how it powers innovative breakthroughs in technology. With the help of Python, learn the fundamentals of transforming data into knowledge and developing algorithms efficiently. This book covers problem-solving with a focus on classification, regression analysis, and clustering. Build your machine learning system for sentiment analysis and embed it into a web app for the world to see. Gain practical knowledge and improve your skillset with the best practices of machine learning.