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Deep Learning from the Basics

Deep Learning from the Basics

Koki Saitoh

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Deep learning is rapidly becoming the most preferred way of solving data problems. This is thanks, in part, to its huge variety of mathematical algorithms and their ability to find patterns that are otherwise invisible to us.

Deep Learning from the Basics begins with a fast-paced introduction to deep learning with Python, its definition, characteristics, and applications. You'll learn how to use the Python interpreter and the script files in your applications, and utilize NumPy and Matplotlib in your deep learning models. As you progress through the book, you'll discover backpropagation--an efficient way to calculate the gradients of weight parameters--and study multilayer perceptrons and their limitations, before, finally, implementing a three-layer neural network and calculating multidimensional arrays.

By the end of the book, you'll have the knowledge to apply the relevant technologies in deep learning.

  • Preface
    • About the Book
      • About the Authors
      • Learning Objectives
      • Audience
      • Approach
  • Introduction
    • Concept of this book
      • For whom is this book written?
      • For whom is this book not written?
      • How to read this book
    • Acknowledgments
  • 1. Introduction to Python
    • What is Python?
    • Installing Python
      • Python Versions
      • External Libraries That We Use
      • Anaconda Distribution
    • Python Interpreter
      • Mathematical Operations
      • Data Types
      • Variables
      • Lists
      • Dictionaries
      • Boolean
      • if Statements
      • for Statements
      • Functions
    • Python Script Files
      • Saving in a File
      • Classes
    • NumPy
      • Importing NumPy
      • Creating a NumPy Array
      • Mathematical Operations in NumPy
      • N-Dimensional NumPy Arrays
      • Broadcasting
      • Accessing Elements
    • Matplotlib
      • Drawing a Simple Graph
      • Features of pyplot
      • Displaying Images
    • Summary
  • 2. Perceptrons
    • What Is a Perceptron?
    • Simple Logic Circuits
      • AND Gate
      • NAND and OR gates
    • Implementing Perceptrons
      • Easy Implementation
      • Introducing Weights and Bias
      • Implementation with Weights and Bias
    • Limitations of Perceptrons
      • XOR Gate
      • Linear and Nonlinear
    • Multilayer Perceptrons
      • Combining the Existing Gates
      • Implementing an XOR Gate
    • From NAND to a Computer
    • Summary
  • 3. Neural Networks
    • From Perceptrons to Neural Networks
      • Neural Network Example
      • Reviewing the Perceptron
      • Introducing an Activation Function
    • Activation Function
      • Sigmoid Function
      • Implementing a Step Function
      • Step Function Graph
      • Implementing a Sigmoid Function
      • Comparing the Sigmoid Function and the Step Function
      • Nonlinear Function
      • ReLU Function
    • Calculating Multidimensional Arrays
      • Multidimensional Arrays
      • Matrix Multiplication
      • Matrix Multiplication in a Neural Network
    • Implementing a Three-Layer Neural Network
      • Examining the Symbols
      • Implementing Signal Transmission in Each Layer
      • Implementation Summary
    • Designing the Output Layer
    • Identity Function and Softmax Function
      • Issues when Implementing the Softmax Function
      • Characteristics of the Softmax Function
      • Number of Neurons in the Output Layer
    • Handwritten Digit Recognition
      • MNIST Dataset
      • Inference for Neural Network
      • Batch Processing
    • Summary
  • 4. Neural Network Training
    • Learning from Data
      • Data-Driven
      • Training Data and Test Data
    • Loss Function
      • Sum of Squared Errors
      • Cross-Entropy Error
      • Mini-Batch Learning
      • Implementing Cross-Entropy Error (Using Batches)
      • Why Do We Configure a Loss Function?
    • Numerical Differentiation
      • Derivative
      • Examples of Numerical Differentiation
      • Partial Derivative
    • Gradient
      • Gradient Method
      • Gradients for a Neural Network
    • Implementing a Training Algorithm
      • A Two-Layer Neural Network as a Class
      • Implementing Mini-Batch Training
      • Using Test Data for Evaluation
    • Summary
  • 5. Backpropagation
    • Computational Graphs
      • Using Computational Graphs to Solve Problems
      • Local Calculation
      • Why Do We Use Computational Graphs?
    • Chain Rule
      • Backward Propagation in a Computational Graph
      • What Is the Chain Rule?
      • The Chain Rule and Computational Graphs
    • Backward Propagation
      • Backward Propagation in an Addition Node
      • Backward Propagation in a Multiplication Node
      • Apples Example
    • Implementing a Simple Layer
      • Implementing a Multiplication Layer
      • Implementing an Addition Layer
    • Implementing the Activation Function Layer
      • ReLU Layer
      • Sigmoid Layer
    • Implementing the Affine and Softmax Layers
      • Affine Layer
      • Batch-Based Affine Layer
      • Softmax-with-Loss Layer
    • Implementing Backpropagation
      • Overall View of Neural Network Training
      • Presupposition
      • Implementing a Neural Network That Supports Backpropagation
      • Gradient Check
      • Training Using Backpropagation
    • Summary
  • 6. Training Techniques
    • Updating Parameters
      • Story of an Adventurer
      • SGD
      • Disadvantage of SGD
      • Momentum
      • AdaGrad
      • Adam
      • Which Update Technique Should We Use?
      • Using the MNIST Dataset to Compare the Update Techniques
    • Initial Weight Values
      • How About Setting the Initial Weight Values to 0?
      • Distribution of Activations in the Hidden Layers
      • Initial Weight Values for ReLU
      • Using the MNIST Dataset to Compare the Weight Initializers
      • Batch Normalization
      • Batch Normalization Algorithm
      • Evaluating Batch Normalization
    • Regularization
      • Overfitting
      • Weight Decay
      • Dropout
    • Validating Hyperparameters
      • Validation Data
      • Optimizing Hyperparameters
      • Implementing Hyperparameter Optimization
    • Summary
  • 7. Convolutional Neural Networks
    • Overall Architecture
    • The Convolution Layer
      • Issues with the Fully Connected Layer
      • Convolution Operations
      • Padding
      • Stride
      • Performing a Convolution Operation on Three-Dimensional Data
      • Thinking in Blocks
      • Batch Processing
    • The Pooling Layer
      • Characteristics of a Pooling Layer
    • Implementing the Convolution and Pooling Layers
      • Four-Dimensional Arrays
      • Expansion by im2col
      • Implementing a Convolution Layer
      • Implementing a Pooling Layer
    • Implementing a CNN
    • Visualizing a CNN
      • Visualizing the Weight of the First Layer
      • Using a Hierarchical Structure to Extract Information
    • Typical CNNs
      • LeNet
      • AlexNet
    • Summary
  • 8. Deep Learning
    • Making a Network Deeper
      • Deeper Networks
      • Improving Recognition Accuracy
      • Motivation for a Deeper Network
    • A Brief History of Deep Learning
      • ImageNet
      • VGG
      • GoogLeNet
      • ResNet
    • Accelerating Deep Learning
      • Challenges to Overcome
      • Using GPUs for Acceleration
      • Distributed Training
      • Reducing the Bit Number for Arithmetic Precision
    • Practical Uses of Deep Learning
      • Object Detection
      • Segmentation
      • Generating Image Captions
    • The Future of Deep Learning
      • Converting Image Styles
      • Generating Images
      • Automated Driving
      • Deep Q-Networks (Reinforcement Learning)
    • Summary
  • Appendix A
    • Computational Graph of the Softmax-with-Loss Layer
      • Forward Propagation
      • Backward Propagation
    • Summary
  • Title:Deep Learning from the Basics
  • Author:Koki Saitoh
  • Original title:Deep Learning from the Basics
  • ISBN:9781800209725, 9781800209725
  • Date of issue:2021-03-08
  • Format:Ebook
  • Item ID: e_2a3d
  • Publisher: Packt Publishing