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    Inside Deep Learning: Math, Algorithms, Models
    Inside Deep Learning: Math, Algorithms, Models
    Inside Deep Learning: Math, Algorithms, Models
    Ebook1,295 pages6 hours

    Inside Deep Learning: Math, Algorithms, Models

    By Edward Raff

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    About this ebook

    Journey through the theory and practice of modern deep learning, and apply innovative techniques to solve everyday data problems.

    In Inside Deep Learning, you will learn how to:

        Implement deep learning with PyTorch
        Select the right deep learning components
        Train and evaluate a deep learning model
        Fine tune deep learning models to maximize performance
        Understand deep learning terminology
        Adapt existing PyTorch code to solve new problems

    Inside Deep Learning is an accessible guide to implementing deep learning with the PyTorch framework. It demystifies complex deep learning concepts and teaches you to understand the vocabulary of deep learning so you can keep pace in a rapidly evolving field. No detail is skipped—you’ll dive into math, theory, and practical applications. Everything is clearly explained in plain English.

    About the technology
    Deep learning doesn’t have to be a black box! Knowing how your models and algorithms actually work gives you greater control over your results. And you don’t have to be a mathematics expert or a senior data scientist to grasp what’s going on inside a deep learning system. This book gives you the practical insight you need to understand and explain your work with confidence.

    About the book
    Inside Deep Learning illuminates the inner workings of deep learning algorithms in a way that even machine learning novices can understand. You’ll explore deep learning concepts and tools through plain language explanations, annotated code, and dozens of instantly useful PyTorch examples. Each type of neural network is clearly presented without complex math, and every solution in this book can run using readily available GPU hardware!

    What's inside

        Select the right deep learning components
        Train and evaluate a deep learning model
        Fine tune deep learning models to maximize performance
        Understand deep learning terminology

    About the reader
    For Python programmers with basic machine learning skills.

    About the author
    Edward Raff is a Chief Scientist at Booz Allen Hamilton, and the author of the JSAT machine learning library.

    Table of Contents
    PART 1 FOUNDATIONAL METHODS
    1 The mechanics of learning
    2 Fully connected networks
    3 Convolutional neural networks
    4 Recurrent neural networks
    5 Modern training techniques
    6 Common design building blocks
    PART 2 BUILDING ADVANCED NETWORKS
    7 Autoencoding and self-supervision
    8 Object detection
    9 Generative adversarial networks
    10 Attention mechanisms
    11 Sequence-to-sequence
    12 Network design alternatives to RNNs
    13 Transfer learning
    14 Advanced building blocks
    LanguageEnglish
    PublisherManning
    Release dateJul 5, 2022
    ISBN9781638357216
    Inside Deep Learning: Math, Algorithms, Models

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      Inside Deep Learning - Edward Raff

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