GitHub - bestdecoder/neural: neural networks, sparse encoders, and recursive auto encoder sin python

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neural networks, sparse encoders, and recursive auto encoder sin python

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Name	Name	Last commit message	Last commit date
Latest commit History 35 Commits 35 Commits
deep	deep
pca	pca
rae	rae
selftaught	selftaught
softmax	softmax
tests	tests
.gitignore	.gitignore
README	README
display_network.py	display_network.py
neurolib.py	neurolib.py
numerical_gradient.py	numerical_gradient.py
sample_images.py	sample_images.py
sparse_autoencoder.py	sparse_autoencoder.py
train_sparse_autoencoder_on_matlab_images.py	train_sparse_autoencoder_on_matlab_images.py
train_sparse_autoencoder_on_mnist.py	train_sparse_autoencoder_on_mnist.py

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This is a rewrite of the tutorial about unsupervised feature learning and deep learning in Python using numpy and scipy.

Tutorial: http://ufldl.stanford.edu/wiki/index.php/UFLDL_Tutorial

I use numpy for matrices, and scipy.optimize package for the L-BFGS minimization algorithm.

Deep learning learns low and high-level features from large amounts of unlabeled data, improving classification on different, labeled, datasets.  Deep learning can achieve an accuracy of 98% on the MNIST dataset.