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.Through reading this book, readers can follow step by step to build a deep learning library from 0 without any deep learning platform. Finally, as a comparison, the use of the Deep Learning platform Pytorch is introduced, so that readers can easily learn to use this deep learning platform, which will help readers understand the design ideas of these platforms more deeply, so as to better grasp and use these deep learning platforms.This book is suitable not only for beginners without any Deep Learning knowledge, but also for practitioners who have experience in using deep learning libraries and want to understand its underlying implementation principles. This book is especially suitable as a Deep Learning textbook for colleges and universities.

Anatomy of Deep Learning Principles: Writing a Deep Learning Library from Scratch PDF

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