Book Image

Deep Learning from the Basics

By : Koki Saitoh

5 (1)

Book Image

Deep Learning from the Basics

5 (1)

By: Koki Saitoh

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

Introduction

Concept of this book

Acknowledgments

Free Chapter

1. Introduction to Python

1. Introduction to Python

What is Python?

Installing Python

Python Interpreter

Python Script Files

2. Perceptrons

What Is a Perceptron?

Simple Logic Circuits

Implementing Perceptrons

Limitations of Perceptrons

Multilayer Perceptrons

From NAND to a Computer

3. Neural Networks

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

4. Neural Network Training

4. Neural Network Training

Learning from Data

Numerical Differentiation

Implementing a Training Algorithm

5. Backpropagation

5. Backpropagation

Computational Graphs

Backward Propagation

Implementing a Simple Layer

Implementing the Activation Function Layer

Implementing the Affine and Softmax Layers

Implementing Backpropagation

6. Training Techniques

6. Training Techniques

Updating Parameters

Initial Weight Values

Validating Hyperparameters

7. Convolutional Neural Networks

7. Convolutional Neural Networks

Overall Architecture

The Convolution Layer

The Pooling Layer

Implementing the Convolution and Pooling Layers

Implementing a CNN

Visualizing a CNN

8. Deep Learning

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

Appendix A

Computational Graph of the Softmax-with-Loss Layer

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What is Python?

Python is a simple programming language that is easy to read and learn. It is open-source software that you can use as you like for free. You can write a program that uses English-like grammar without the time-consuming compilation. This makes Python easy to use and, therefore a great choice for beginner programmers. In fact, many computer science courses in universities and professional schools choose Python as the first language they teach.

Python enables you to write both readable and high-performance (fast) code. If massive data processing and high-speed responses are required, Python will meet your needs. This is why Python is a favorite both with beginners and professionals. Cutting-edge IT companies such as Google, Microsoft, and Facebook frequently use Python.

Python is often used in scientific fields, particularly in machine learning and data science. Because of its high performance and excellent libraries for numerical calculations and statistical processing (NumPy and SciPy, for example), Python occupies a solid position in the realm of data science. It is often used as the backbone of deep learning frameworks such as Caffe, TensorFlow, and PyTorch, which provide Python interfaces. Therefore, learning Python is also useful when you wish to use a framework for deep learning.

Python is an optimal programming language, particularly in data science, as it offers various user-friendly and efficient features for beginners and professionals alike. For these reasons, it is the natural choice for achieving the goal of this book: Deep Learning from the Basics.