Deep Learning from Scratch: Building with Python from First Principles
Book Name: Deep Learning from Scratch: Building with Python from First Principles
Author: Seth Weidman
Publisher: O’Reilly Media
ISBN-10: 978-1492041412,1492041416
Year: 2019
Pages: 252 Pages
Language: English
File size: 5 MB
File format: PDF
Deep Learning from Scratch: Building with Python from First Principles Pdf Book Description:
As you know that python is one of the most programming language in the world and you can build almost all software using python. and you know that this book comes with several chapters that you have to read to know more about python. This book is here to help you get your job done. In general, if example code is offered with this book, you may use it in your programs and documentation. You do not need to contact us for permission unless you’re reproducing a significant portion of the code. For example, writing a program that uses several chunks of code from this book does not require permission. Selling or distributing a CD-ROM of examples from O’Reilly books does require permission. Answering a question by citing this book and quoting example code does not require permission. Incorporating a significant amount of example code from this book into your product’s documentation does require permission.
If you’ve tried to learn about neural networks and deep learning, you’ve probably encountered an abundance of resources, from blog posts to MOOCs (massive open online courses, such as those offered on Coursera and Udacity) of varying quality and even some books—I know I did when I started exploring the subject a few years ago. However, if you’re reading this preface, it’s likely that each explanation of neural networks that you’ve come across is lacking in some way. I found the same thing when I started learning: the various explanations were like blind men describing different parts of an elephant, but none describing the whole thing. That is what led me to write this book. These existing resources on neural networks mostly fall into two categories. Some are conceptual and mathematical, containing both the drawings one typically finds in explanations of neural networks, of circles connected by lines with arrows on the ends, as well as extensive mathematical explanations of what is going on so you can “understand the theory.” A prototypical example of this is the very good book Deep Learning by Ian Goodfellow et al. (MIT Press).
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