This book introduces the basic principles and implementation process of deep learning algorithms 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 explanation with code implementation from scratch can enable readers to better understand the basic principles of deep learning and the design ideas of popular deep learning libraries.
My deep learning book “Anatomy of Deep Learning Principles - Writing a Deep Learning Library from Scratch” has been translated into English, Japanese, and electronically published on leanpub.com and www.amazon.com. Friends are welcome to buy and provide suggestions and opinions, thank you.
Read free Sample here: Anatomy of deep learning pdf
eBooks in different languages
English version: https://leanpub.com/dle
- Japanese version: https://leanpub.com/dl_jp
- Chinese version: https://leanpub.com/dl_0
- Amazon: ekindle version and paperback
Traditional Chinese： https://leanpub.com/dl_tw
- Frech version:
github link for code: