Math for Deep Learning: What You Need to Know to Understand Neural Networks

Length: 344 pages
Edition: 1
Language: English
Publisher: No Starch Press
Publication Date: 2021-11-23
ISBN-10: 1718501900
ISBN-13: 9781718501904
Sales Rank: #671330 (See Top 100 Books)

Math for Deep Learning provides the essential math you need to understand deep learning discussions, explore more complex implementations, and better use the deep learning toolkits.

With Math for Deep Learning, you’ll learn the essential mathematics used by and as a background for deep learning.

You’ll work through Python examples to learn key deep learning related topics in probability, statistics, linear algebra, differential calculus, and matrix calculus as well as how to implement data flow in a neural network, backpropagation, and gradient descent. You’ll also use Python to work through the mathematics that underlies those algorithms and even build a fully-functional neural network.

In addition you’ll find coverage of gradient descent including variations commonly used by the deep learning community: SGD, Adam, RMSprop, and Adagrad/Adadelta.

Cover Page
Title Page
Copyright Page
Dedication
About the Author
About the Technical Reviewer
BRIEF CONTENTS
CONTENTS IN DETAIL
FOREWORD
ACKNOWLEDGMENTS
INTRODUCTION
    Who Is This Book For?
    About This Book
1 SETTING THE STAGE
    Installing the Toolkits
    NumPy
    SciPy
    Matplotlib
    Scikit-Learn
    Summary
2 PROBABILITY
    Basic Concepts
    The Rules of Probability
    Joint and Marginal Probability
    Summary
3 MORE PROBABILITY
    Probability Distributions
    Bayes’ Theorem
    Summary
4 STATISTICS
    Types of Data
    Summary Statistics
    Quantiles and Box Plots
    Missing Data
    Correlation
    Hypothesis Testing
    Summary
5 LINEAR ALGEBRA
    Scalars, Vectors, Matrices, and Tensors
    Arithmetic with Tensors
    Summary
6 MORE LINEAR ALGEBRA
    Square Matrices
    Eigenvectors and Eigenvalues
    Vector Norms and Distance Metrics
    Principal Component Analysis
    Singular Value Decomposition and Pseudoinverse
    Summary
7 DIFFERENTIAL CALCULUS
    Slope
    Derivatives
    Minima and Maxima of Functions
    Partial Derivatives
    Gradients
    Summary
8 MATRIX CALCULUS
    The Formulas
    The Identities
    Jacobians and Hessians
    Some Examples of Matrix Calculus Derivatives
    Summary
9 DATA FLOW IN NEURAL NETWORKS
    Representing Data
    Data Flow in Traditional Neural Networks
    Data Flow in Convolutional Neural Networks
    Summary
10 BACKPROPAGATION
    What Is Backpropagation?
    Backpropagation by Hand
    Backpropagation for Fully Connected Networks
    Computational Graphs
    Summary
11 GRADIENT DESCENT
    The Basic Idea
    Stochastic Gradient Descent
    Momentum
    Adaptive Gradient Descent
    Summary
    Epilogue
APPENDIX: GOING FURTHER
    Probability and Statistics
    Linear Algebra
    Calculus
    Deep Learning
INDEX

AI & Machine Learning Calculus Mathematics Neural Networks Pure Mathematics

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