simplified-deeplearning's Introduction

Simplified DeepLearning

Quick and dirty implementations of some interesting works related to deep learning.

Gradient Descent

Regression

MLP from scratch

Regularization

SGD Comparison

Comparison of various SGD algorithms on logistic regression and MLP. The relation of these algorithms is shown in the following figure, please refer to sgd-comparison for the details.

Newton Method

Neural Style

Refer to jcjohnson.

Function Approximation

Use neural network to approximate functions. The approximated functions are shown in the following figures, please refer to function-approximation for the details.

Char-RNN

Refer to karpathy or johnarevalo.

POS Tagging

PCA, PPCA

Autoencoder

Refer to Keras.

Word Embedding

MCMC

RBM

Generative Adversarial Networks

A simple demonstration of Generative Adversarial Networks (GAN), maybe problematic.

According to the [paper](https://arxiv.org/abs/1406.2661), we also use GAN to generate Gaussian distribution which shown in the left figure. Then we try to generate digits based on MNIST dataset, however, we encouter "the Helvetica scenario" in which G collapses too many values of z to the same value of x. Nevertheless, it is a simple demonstration, please see the [details](https://github.com/SwordYork/simplified-deeplearning/tree/master/GAN).

Updating.... Please help us to implement these examples, we need a simplified implementation. Or you have other nice examples, please add to this list.

Recommend Projects