designing next-generation machine intelligence algorithms Buduma, Nithin creator author. Buduma, Nikhil author. Papa Joe author. Locascio, Nicholas. text bibliography cau New Delhi Shroff Publishers and Distributors Pvt.Ltd. 2022 Second edition. monographic eng

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xiii, 372 pages : illustrations ; 24 cm We're in the midst of an AI research explosion. Deep learning has unlocked superhuman perception to power our push toward creating self-driving vehicles, defeating human experts at a variety of difficult games including Go, and even generating essays with shockingly coherent prose. But deciphering these breakthroughs often takes a PhD in machine learning and mathematics. The updated second edition of this book describes the intuition behind these innovations without jargon or complexity. Fundamentals of linear algebra for deep learning -- Fundamentals of probability -- The neural network -- Training feed-forward neural networks -- Implementing neural networks in PyTorch -- Beyond gradient descent -- Convolutional neural networks -- Embedding and representation learning -- Models for sequence analysis -- Generative models -- Methods in interpretability -- Memory augmented neural networks -- Deep reinforcement learning. Nithin Buduma, Nikhil Buduma, and Joe Papa ; with contributions by Nicholas Locascio. Previous edition: published as by Nikhil Buduma with contributions by Nicholas Locascio. 2017. Includes bibliographical references and index. CSE ps REF Omni concept Artificial intelligence Machine learning Neural networks (Computer science) Artificial intelligence Machine learning Neural networks (Computer science) 006.31 B9279f 9781492082187 9789355420121 UKMGB 260517 20260517150221.0 57803 eng