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The Neural Network Zoo - The Asimov Institute With new neural network architectures popping up every now and then, it’s hard to keep track of them all. Knowing all the abbreviations being thrown around (DCIGN, BiLSTM, DCGAN, anyone?) can be a bit overwhelming at first. So I decided to compose a cheat sheet containing many of those architectures. Most of these are neural networks, some are completely different beasts. Though all of these architectures are presented as novel and unique, when I drew the node structures… their underlying relations started to make more sense.

dnngraph by ajtulloch It consists of several parts: A DSL for specifying the model. This uses the lens library for elegant, composable constructions, and the fgl graph library for specifying the network layout. 2. Overview — How to Tango with Django 1.7 The aim of this book is to provide you with a practical guide to web development using Django 1.7. The book is designed primarily for students, providing a walkthrough of the steps involved in getting your first web applications up and running, as well as deploying them to a web server. This book seeks to complement the official Django Tutorials and many of the other excellent tutorials available online. By putting everything together in one place, this book fills in many of the gaps in the official Django documentation providing an example-based design driven approach to learning the Django framework. Furthermore, this book provides an introduction to many of the aspects required to master web application development.

Artificial Neural Networks: Mathematics of Backpropagation (Part 4) — BRIAN DOLHANSKY No longer is there a linear relation in between a change in the weights and a change of the target. Any perturbation at a particular layer will be further transformed in successive layers. So, then, how do we compute the gradient for all weights in our network? This is where we use the backpropagation algorithm. Hacker's guide to Neural Networks 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 ConvNetJS - a Javascript library for training Neural Networks. Javascript allows one to nicely visualize what’s going on and to play around with the various hyperparameter settings, but I still regularly hear from people who ask for a more thorough treatment of the topic. This article (which I plan to slowly expand out to lengths of a few book chapters) is my humble attempt. It’s on web instead of PDF because all books should be, and eventually it will hopefully include animations/demos etc. 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.

A Neural Network Playground Data Which dataset do you want to use? Features Which properties do you want to feed in? Click anywhere to edit. Weight/Bias is 0.2. Recurrent Neural Networks Tutorial, Part 1 – Introduction to RNNs – WildML Recurrent Neural Networks (RNNs) are popular models that have shown great promise in many NLP tasks. But despite their recent popularity I’ve only found a limited number of resources that throughly explain how RNNs work, and how to implement them. That’s what this tutorial is about.

Yes you should understand backprop – Andrej Karpathy – Medium When we offered CS231n (Deep Learning class) at Stanford, we intentionally designed the programming assignments to include explicit calculations involved in backpropagation on the lowest level. The students had to implement the forward and the backward pass of each layer in raw numpy. Inevitably, some students complained on the class message boards:

Understanding Convolutional Neural Networks for NLP – WildML When we hear about Convolutional Neural Network (CNNs), we typically think of Computer Vision. CNNs were responsible for major breakthroughs in Image Classification and are the core of most Computer Vision systems today, from Facebook’s automated photo tagging to self-driving cars. More recently we’ve also started to apply CNNs to problems in Natural Language Processing and gotten some interesting results. In this post I’ll try to summarize what CNNs are, and how they’re used in NLP. The intuitions behind CNNs are somewhat easier to understand for the Computer Vision use case, so I’ll start there, and then slowly move towards NLP. What is Convolution?

A Visual and Interactive Guide to the Basics of Neural Networks – J Alammar – Explorations in touchable pixels and intelligent androids Motivation I’m not a machine learning expert. I’m a software engineer by training and I’ve had little interaction with AI. I had always wanted to delve deeper into machine learning, but never really found my “in”. That’s why when Google open sourced TensorFlow in November 2015, I got super excited and knew it was time to jump in and start the learning journey.

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