Top Deep Learning Projects

A list of popular github projects related to deep learning (ranked by stars).

Last Update: 2016.08.09

Project Name
Stars
Description

TensorFlow
29622
Computation using data flow graphs for scalable machine learning.

Caffe
11799
Caffe: a fast open framework for deep learning.

Neural Style
10148
Torch implementation of neural style algorithm.

Deep Dream
9042
Deep Dream.

Keras
7502
Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on Theano and TensorFlow.

Roc AlphaGo
7170
An independent, student-led replication of DeepMind’s 2016 Nature publication,

This series of post will list some good articles about how to implement a neural network. Thanks for the authors for the excellent work.
If you are the author and you don’t want your articles listed here. Please email to learn4master, we will remove it from the site.

How to implement a neural network Part 1

From: http://peterroelants.github.io/posts/neural_network_implementation_part01/

This page is part of a 5 (+2) parts tutorial on how to implement a simple neural network model. You can find the links to the rest of the tutorial here:

• Part 1: Linear regression
• Intermezzo 1: Logistic classification function
• Part 2: Logistic regression (classification)
• Part 3: Hidden layer
• Intermezzo 2: Softmax classification function
• Part 4: Vectorization
• Part 5: Generalization of multiple layers

The tutorials are generated from Python 2 IPython Notebook files,

Vectorization

This part will cover:

• Vectorization of backpropagation
• Gradient checking
• Momentum

The previous tutorial described a very simple neural network with only one input, one hidden neuron and one output. This tutorial will describe a neural network that takes 2-dimensional input samples, projects them onto a 3-dimensional hidden layer, and classifies them with a 2-dimensional softmax output classfier, this softmax function is explained in intermezzo 2 . While we didn’t add the bias parameters to the previous 2 models, we will add them to this model.

Machine Learning¶

• An Introduction to Machine Learning Theory and Its Applications: A Visual Tutorial with Examples
• A Gentle Guide to Machine Learning
• A Visual Introduction to Machine Learning
• Introduction to Machine Learning

Deep Learning & Neural Networks

• An Introduction to Neural Networks
• An Introduction to Image Recognition with Deep Learning
• Neural Networks and Deep Learning

Tensor flow examples:

TensorFlow Examples

TensorFlow Tutorial with popular machine learning algorithms implementation.

from:http://iamtrask.github.io/2015/07/12/basic-python-network/

Summary: I learn best with toy code that I can play with. This tutorial teaches backpropagation via a very simple toy example, a short python implementation.

Edit: Some folks have asked about a followup article, and I’m planning to write one. I’ll tweet it out when it’s complete at @iamtrask. Feel free to follow if you’d be interested in reading it and thanks for all the feedback!

Just Give Me The Code:

view sourceprint?

01.X = np.array([ [0,0,1],[0,1,1],[1,0,1],[1,1,1] ])
02.y = np.array([[0,1,1,0]]).T
03.syn0 = 2*np.random.random((3,4)) - 1
04.syn1 = 2*np.random.random((4,1)) - 1
05.for j in xrange(60000):
06.l1 = 1/(1+np.exp(-(np.dot(X,syn0))))
07.l2 = 1/(1+np.exp(-(np.dot(l1,syn1))))
08.l2_delta = (y - l2)*(l2*(1-l2))
09.l1_delta = l2_delta.dot(syn1.T) * (l1 * (1-l1))
10.syn1 += l1.T.dot(l2_delta)
11.syn0 += X.T.dot(l1_delta)

However,

Calculus on Computational Graphs: Backpropagation

http://colah.github.io/posts/2015-08-Backprop/

Introduction

Backpropagation is the key algorithm that makes training deep models computationally tractable. For modern neural networks, it can make training with gradient descent as much as ten million times faster, relative to a naive implementation. That’s the difference between a model taking a week to train and taking 200,000 years.

Beyond its use in deep learning, backpropagation is a powerful computational tool in many other areas, ranging from weather forecasting to analyzing numerical stability – it just goes by different names. In fact, the algorithm has been reinvented at least dozens of times in different fields (see Griewank (2010)).

A Step by Step Backpropagation Example

from:https://mattmazur.com/2015/03/17/a-step-by-step-backpropagation-example/

Background

Backpropagation is a common method for training a neural network. There is no shortage of papers online that attempt to explain how backpropagation works, but few that include an example with actual numbers. This post is my attempt to explain how it works with a concrete example that folks can compare their own calculations to in order to ensure they understand backpropagation correctly.

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