Anatomy of Deep Learning Principles
Anatomy of Deep Learning Principles
Writing a Deep Learning Library from Scratch
About the Book
This book introduces the basic principles and implementation process of deep learning in a simple way, and uses python's numpy library to build its own deep learning library from scratch instead of using existing deep learning libraries. On the basis of introducing basic knowledge of Python programming, calculus, and probability statistics, the core basic knowledge of deep learning such as regression model, neural network, convolutional neural network, recurrent neural network, and generative network is introduced in sequence according to the development of deep learning. While analyzing the principle in a simple way, it provides a detailed code implementation process. It is like not teaching you how to use weapons and mobile phones, but teaching you how to make weapons and mobile phones by yourself. This book is not a tutorial on the use of existing deep learning libraries, but an analysis of how to develop deep learning libraries from 0. This method of combining the principle from 0 with code implementation can enable readers to better understand the basic principles of deep learning and the design ideas of popular deep learning libraries.
Table of Contents
Chapter 1 Programming and Math Fundamentals

1.1.2 Object, print() function, type conversion, comment, variable, input() function
[subscript operator []](#subscriptoperator)


 1. array()
 2. Multidimensional array type ndarray
 3. asarray()
 4. The tolist() method of ndarray
 5. astype() and reshape()
 6. arange() and linspace()
 7. full(), empty(), zeros(), ones(), eye()
 8. Common functions for creating tensors of random values
 9. Add, Repeat & Lay, Merge & Split, Edge Fill, Add Axis & Swap Axis
 Repeat repeat()
 laying tile()
 merge concatenate()
 overlay stack()
 column_stack(), hstack(), vstack()
 split split()
 Edge Padding
 Add Axis
 Swap axes

Chapter 3 Linear Regression, Logistic Regression and Softmax Regression


Chapter 5 Basic Techniques for Improving Neural Network Performance
Chapter 7 Recurrent Neural Network RNN
7.1 Sequence problems and models
7.4 Implementation of singlelayer recurrent neural network
7.6 Gradient explosion and gradient disappearance of RNN network
7.9 Class Representation and Implementation of Recurrent Neural Network
 7.9.1 Implementing Recurrent Neural Networks with Classes
 7.9.2 Class implementation of recurrent neural network unit
 7.10 Multilayer, Bidirectional Recurrent Neural Network
 7.10.1 Multilayer Recurrent Neural Network
 7.10.2 Training and prediction of multilayer recurrent neural network
 7.10.3 Bidirectional Recurrent Neural Network
7.11 Sequence to sequence (seq2seq) model

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