Fundamentals of Deep Learning, 2nd Edition

Designing Next-Generation Machine Intelligence Algorithms

Author: Nithin Buduma

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    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. Python-proficient programmers, software engineering professionals, and computer science majors will be able to reimplement these breakthroughs on their own and reason about them with a level of sophistication that rivals some of the best developers in the field.

    • Learn the mathematics behind machine learning jargon
    • Examine the foundations of machine learning and neural networks
    • Manage problems that arise as you begin to make networks deeper
    • Build neural networks that analyze complex images
    • Perform effective dimensionality reduction using autoencoders
    • Dive deep into sequence analysis to examine language
    • Explore methods in interpreting complex machine learning models
    • Gain theoretical and practical knowledge on generative modeling
    • Understand the fundamentals of reinforcement learning
    Nithin Buduma
    Author 2
    Nikhil Buduma
    Author 3
    Joe Papa
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    Table of Contents (9789355420121_toc.pdf, 113 Kb) [Download]

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