Hacker's guide to Neural Networks - 1
from : http://karpathy.github.io/neuralnets/
Note: this is now a very old tutorial that I’m leaving up, but I don’t believe should be referenced or used. Better materials include CS231n course lectures, slides, and notes, or the Deep Learning book.
Hi there, I’m a CS PhD student at Stanford. I’ve worked on Deep Learning for a few years as part of my research and among several of my related pet projects is
My personal experience with Neural Networks is that everything became much clearer when I started ignoring full-page, dense derivations of backpropagation equations and just started writing code. Thus, this tutorial will contain very little math (I don’t believe it is necessary and it can sometimes even obfuscate simple concepts). Since my background is in Computer Science and Physics, I will instead develop the topic from what I refer to as hackers’s perspective
“…everything became much clearer when I started writing code.”
You might be eager to jump right in and learn about Neural Networks, backpropagation, how they can be applied to datasets in practice, etc. But before we get there, I’d like us to first forget about all that. Let’s take a step back and understand what is really going on at the core. Lets first talk about real-valued circuits.
Update note: I suspended my work on this guide a while ago and redirected a lot of my energy to teaching CS231n (Convolutional Neural Networks) class at Stanford. The notes are on cs231.github.io and the course slides can be found here. These materials are highly related to material here, but more comprehensive and sometimes more polished.
next:https://www.cnblogs.com/zhangzhiwei122/p/15887306.html