Machine Learning, Animated
by
Mark Liu
Book Details
Format
Paperback / Softback
ISBN-10
1032462132
ISBN-13
9781032462134
Publisher
Taylor & Francis Ltd
Imprint
Chapman & Hall/CRC
Country of Manufacture
GB
Country of Publication
GB
Publication Date
Nov 27th, 2025
Print length
436 Pages
Weight
940 grams
Product Classification:
The ArtsProbability & statisticsProbability and statisticsDigital and Information technology: general topicsInformation technology: general issuesDigital animationAlgorithms & data structuresAlgorithms and data structuresGames development & programmingGames development and programmingProgramming & scripting languages: generalProgramming and scripting languages: generalSoftware EngineeringData miningArtificial intelligenceArtificial intelligence (AI)
Ksh 15,100.00
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The release of ChatGPT has kicked off an arms race in Machine Learning (ML), however ML has also been described as a black box and very hard to understand. This book eases you into basic ML concepts and summarises the learning process in three words: initialize, adjust and repeat.
The release of ChatGPT has kicked off an arms race in Machine Learning (ML), however ML has also been described as a black box and very hard to understand. Machine Learning, Animated eases you into basic ML concepts and summarizes the learning process in three words: initialize, adjust and repeat. This is illustrated step by step with animation to show how machines learn: from initial parameter values to adjusting each step, to the final converged parameters and predictions. This book teaches readers to create their own neural networks with dense and convolutional layers, and use them to make binary and multi-category classifications. Readers will learn how to build deep learning game strategies and combine this with reinforcement learning, witnessing AI achieve super-human performance in Atari games such as Breakout, Space Invaders, Seaquest and Beam Rider. Written in a clear and concise style, illustrated with animations and images, this book is particularly appealing to readers with no background in computer science, mathematics or statistics. Access the book's repository at: https://github.com/markhliu/MLA
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