Description:
Packed with clear explanations, visualizations, and working examples, the book covers essential machine learning techniques in depth, along with two cutting-edge machine learning techniques: transformers and graph neural networks.
Brief description: 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.
Review Quotes:
"I'm confident that you will find this book invaluable both as a broad overview of the exciting field of machine learning and as a treasure of practical insights. I hope it inspires you to apply machine learning for the greater good in your problem area, whatever it might be."
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Dmytro Dzhulgakov, PyTorch Core Maintainer
"This 700-page book covers most of today's widely used machine learning algorithms, and will be especially useful to anybody who wants to understand modern machine learning through examples of working code. It covers a variety of approaches, from basic algorithms such as logistic regression to very recent topics in deep learning such as BERT and GPT language models and generative adversarial networks. The book provides examples of nearly every algorithm it discusses in the convenient form of downloadable Jupyter notebooks that provide both code and access to datasets. Importantly, the book also provides clear instructions on how to download and start using state-of-the-art software packages that take advantage of GPU processors, including PyTorch and Google Colab."
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Tom Mitchell, Professor CMU, Founder of CMU's Machine Learning Department