12 Machine Learning Books You Should Read in 2023
This complements the list that I posted earlier under the title “Math for Machine Learning: 14 Must-Read Books”, available here. Many of the following books have a free PDF version, their own website and GitHub repository, and usually you can purchase the print version. Some are self-published, with the PDF version regularly updated, and even […]
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Top 30 Machine Learning Influencers to Follow in 2023
This list is based on LinkedIn. Criteria for selection include the number of followers (above 50k), the relevancy and contributions to the field, relevant education and professional experience, as well as recent activity. To submit an entry, email us at vincentg@MLTechniques.com, with the name of the candidate, a short bio, and a link to his/her […]
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Math for Machine Learning: 14 Must-Read Books
It is possible to design and deploy advanced machine learning algorithms that are essentially math-free and stats-free. People working on that are typically professional mathematicians. These algorithms are not necessarily simpler. See for instance a math-free regression technique with prediction intervals, here. Or supervised classification and alternative to t-SNE, here. Interestingly, this latter math-free machine […]
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New Book: Efficient Deep Learning
Subtitled “Fast, smaller, and better models”. This book goes through algorithms and techniques used by researchers and engineers at Google Research, Facebook AI Research (FAIR), and other eminent AI labs to train and deploy their models on devices ranging from large server-side machines to tiny microcontrollers. The book presents a balance of fundamentals as well […]
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