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Probabilistic Machine Learning: An Introduction (Adaptive Computation and Machine Learning series)

Name: Probabilistic Machine Learning: An Introduction (Adaptive Computation and Machine Learning series)
Price: 107.86 USD
Availability: InStock
Rating: 4.4 (160 reviews)

4.4 of 5 Stars: Computer Science - Probabilistic Machine Learning: An Introduction (Adaptive Computation and Machine Learning series)

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Book Synopsis: A detailed and up-to-date introduction to machine learning, presented through the unifying lens of probabilistic modeling and Bayesian decision theory.

This book offers a detailed and up-to-date introduction to machine learning (including deep learning) through the unifying lens of probabilistic modeling and Bayesian decision theory. The book covers mathematical background (including linear algebra and optimization), basic supervised learning (including linear and logistic regression and deep neural networks), as well as more advanced topics (including transfer learning and unsupervised learning). End-of-chapter exercises allow students to apply what they have learned, and an appendix covers notation.

Probabilistic Machine Learning grew out of the author’s 2012 book, Machine Learning: A Probabilistic Perspective. More than just a simple update, this is a completely new book that reflects the dramatic developments in the field since 2012, most notably deep learning. In addition, the new book is accompanied by online Python code, using libraries such as scikit-learn, JAX, PyTorch, and Tensorflow, which can be used to reproduce nearly all the figures; this code can be run inside a web browser using cloud-based notebooks, and provides a practical complement to the theoretical topics discussed in the book. This introductory text will be followed by a sequel that covers more advanced topics, taking the same probabilistic approach.

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Looking to enhance your understanding of machine learning? Look no further than Probabilistic Machine Learning: An Introduction! This comprehensive and up-to-date book takes a fresh approach to the world of machine learning by presenting it through the lens of probabilistic modeling and Bayesian decision theory. With a strong focus on mathematical background, basic and advanced topics, and practical exercises, this book is a must-have for both beginners and experienced learners.

What sets Probabilistic Machine Learning apart from other textbooks is its coverage of the latest advancements in the field, particularly deep learning. This completely new edition reflects the significant developments that have taken place since 2012, ensuring that you are equipped with the most cutting-edge knowledge. Plus, with the inclusion of online Python code, you can easily reproduce and experiment with the concepts discussed in the book, making your learning experience even more enriching.

With Probabilistic Machine Learning, you'll not only gain a solid foundation in machine learning, but you'll also learn how to apply your knowledge to real-world problems. Whether you're interested in linear and logistic regression, deep neural networks, transfer learning, or unsupervised learning, this book covers it all.

Take your machine learning journey to the next level by diving into the world of probabilistic modeling. Don't miss out on this transformative learning opportunity. Grab your copy of Probabilistic Machine Learning: An Introduction today and kick-start your path to becoming a machine learning expert!

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