Deep Learning from the Basics

By : Koki Saitoh

5 (1)

Buy this Book

Deep Learning from the Basics

5 (1)

By: Koki Saitoh

Buy this Book

Overview of this book

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

Introduction

Concept of this book

Acknowledgments

Free Chapter

1. Introduction to Python

NumPy

Summary

2. Perceptrons

What Is a Perceptron?

Simple Logic Circuits

Implementing Perceptrons

Limitations of Perceptrons

Multilayer Perceptrons

From NAND to a Computer

Summary

3. Neural Networks

From Perceptrons to Neural Networks

Activation Function

Calculating Multidimensional Arrays

Implementing a Three-Layer Neural Network

Designing the Output Layer

Identity Function and Softmax Function

Handwritten Digit Recognition

Summary

4. Neural Network Training

Learning from Data

Loss Function

Numerical Differentiation

Gradient

Implementing a Training Algorithm

Summary

5. Backpropagation

Computational Graphs

Chain Rule

Backward Propagation

Implementing a Simple Layer

Implementing the Activation Function Layer

Implementing the Affine and Softmax Layers

Implementing Backpropagation

Summary

6. Training Techniques

Updating Parameters

Initial Weight Values

Regularization

Validating Hyperparameters

Summary

7. Convolutional Neural Networks

Overall Architecture

The Convolution Layer

The Pooling Layer

Implementing the Convolution and Pooling Layers

Implementing a CNN

Visualizing a CNN

Typical CNNs

Summary

8. Deep Learning

Making a Network Deeper

A Brief History of Deep Learning

Accelerating Deep Learning

Practical Uses of Deep Learning

The Future of Deep Learning

Summary

Appendix A

Computational Graph of the Softmax-with-Loss Layer

Summary

Customer Reviews

5 (1)

5 star

100%

4 star

3 star

2 star

1 star

Summary

This chapter described neural network training. First, we introduced a score called a loss function so that a neural network can learn. The goal of neural network training is to discover the weight parameters that lead to the smallest value of the loss function. Then, we learned how to use the gradient of a function, called the gradient method, to discover the smallest loss function value. This chapter covered the following points:

In machine learning, we use training data and test data.
Training data is used for training, while test data is used to evaluate the generalization capability of the trained model.
A loss function is used as a score in neural network training. Weight parameters are updated so that the value of the loss function will decrease.
To update the weight parameters, their gradients are used to update their values in the gradient direction repeatedly.
Calculating a derivative based on the difference when very small values are provided...

Deep Learning from the Basics

By : Koki Saitoh

Deep Learning from the Basics

By: Koki Saitoh

Overview of this book

Related Content you might be interested in

Current Title:

Deep Learning from the Basics

Practical Convolutional Neural Networks

Hands-On Computer Vision with TensorFlow 2

Hands-On Convolutional Neural Networks with TensorFlow

Summary