MATHEMATICS FOR AI AND MACHINE LEARNING — Comprehensive Mathematical Reference for Artificial Intelligence and Machine Learning: https://
amzn.to/4y4YNuU (595 pages)
@kirkdborne
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Mathematical Reference Covers AI and Machine Learning Foundations
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The Laws of Thought Compares AI’s Mathematical Roots With Human Minds
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"The Laws of Thought: The Quest for a Mathematical Theory of the Mind" — explores the quest to use mathematics to describe the ways we think, the ideas behind modern AI systems, and the ways in which they still differ from human minds. Get it at https://
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MIT Press Guide Applies Machine Learning to Large Data Streams
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Machine Learning for Data Streams, with Practical Examples in Massive Online Analysis (a volume in the MIT Press Adaptive Computation and Machine Learning series): https://
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Computer Vision Guide Covers PyTorch Deep Learning and Generative AI
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2nd Edition, 746 pages, massive! Modern Computer Vision with PyTorch Deep Learning — from practical fundamentals to advanced applications and Generative AI: https://
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Understand the inner workings of various neural -
Data Mining Guide Applies Machine Learning to Sales and CRM
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See the Algorithms of Data Science and Machine Learning in real practical business application contexts in this impressively educational classic book: “Data Mining Techniques for Marketing, Sales and CRM”: https://
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Updated AI Textbook Covers Deep Learning, Causality and Multi-Agent Systems
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UPDATED 4th edition of this classic AI textbook [1166 pages] now covers Deep Learning, Transfer Learning, multi-agent systems, robotics, NLProc, causality & much more! “Artificial Intelligence — A Modern Approach” See the book at https://
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Transfer Learning Helps Models Adapt Using Auxiliary Data and Models
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TRANSFER LEARNING book [390 pages]: https://
amzn.to/3R4G0zm Amazon Summary:
"Transfer learning deals with how systems can quickly adapt themselves to new situations, tasks and environments. It gives machine learning systems the ability to leverage auxiliary data and models to -
Scikit-Learn Cookbook Covers Preprocessing, Workflows and Dimensionality
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Scikit-learn Cookbook — 80+ recipes for Machine Learning in Python with scikit-learn [3rd Edition]: https://
amzn.to/4oDGOq7 v/ @PacktDataML 𝓒𝓸𝓷𝓽𝓮𝓷𝓽𝓼:
Common Conventions & API Elements of Scikit-Learn
Pre-Model Workflow and Data Preprocessing
Dimensionality -
A Reasoning Model Guide Implements Core Methods From Scratch
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Build a Reasoning Model (From Scratch): https://
amzn.to/3R4UMqo by @rasbt via @ManningBooks Amazon summary: The book is especially useful because it implements the core methods from scratch rather than treating them as black-box library calls. Readers see how -
Practical Unsupervised Learning Finds Patterns Without Labels
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Insights Discovery from Data Without Labels — Practical Unsupervised Machine Learning: https://
amzn.to/4g3ezz0 via @ManningBooks