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adversarialnetspapers

The classical papers and codes about generative adversarial nets

awesome-deep-learning-papers

awesome-rnn

Recurrent Neural Network - A curated list of resources dedicated to RNN

awesome_tensorflow_implementations

char-rnn-tensorflow

Multi-layer Recurrent Neural Networks (LSTM, RNN) for character-level language models in Python using Tensorflow

convolutional-neural-network-for-power-consumption-classification-using-keras-and-tensorflow

This simple code creates a deep neural network using keras running on top of tensorflow. The user has the option of selecting a 1D convolutional neural network or a standard deep neural network. The DNN is implemented to classify power consumption into weekends and weekdays. This is only implemented for a small number of training examples (hence the moderate performance...). A larger data set is needed in order to achieve adaquete performance. In order to run the code, tensorflow and keras must first be installed.

cryptocurrencyprediction

Predict Cryptocurrency Price with Deep Learning

dart_analysis_python

data-home-price-prediction

Python and tensor flow code to adapt home price predictions from Kaggle

dcgan-completion.tensorflow

Image Completion with Deep Learning in TensorFlow

dcgan-tensorflow

A tensorflow implementation of "Deep Convolutional Generative Adversarial Networks"

deep-rl-tensorflow

TensorFlow implementation of Deep Reinforcement Learning papers

dl_tutorials_10weeks

Deep Learning Tutorials for 10 Weeks

dqn-tensorflow

Tensorflow implementation of Human-Level Control through Deep Reinforcement Learning

fcn.tensorflow

Tensorflow implementation of Fully Convolutional Networks for Semantic Segmentation (http://fcn.berkeleyvision.org)

ganhacks

starter from "How to Train a GAN?" at NIPS2016

google-interview-university

A complete daily plan for studying to become a Google software engineer.

igan

iGAN: a deep learning software that easily generates images with a few brushstrokes (from UC Berkeley and Adobe CTL)

individual-household-electric-power-consumption-data-set-

Individual household electric-power consumption Data Set (LSTM) [tutorial]

ipython-notebooks

A collection of IPython notebooks covering various topics.

models

Models built with TensorFlow

pix2pix-tensorflow

Tensorflow port of Image-to-Image Translation with Conditional Adversarial Nets https://phillipi.github.io/pix2pix/

power-consumption-prediction

Based on the historical electricity consumption data of more than 1000 enterprises in a high-tech Zone, design algorithms to predict the daily total electricity consumption of the Zone in next month (next 30 days)

pydensecrf

Python wrapper to Philipp Krähenbühl's dense (fully connected) CRFs with gaussian edge potentials.

pythondatasciencehandbook

Jupyter Notebooks for the Python Data Science Handbook

reinforcement-learning-with-tensorflow

Simple Reinforcement learning tutorials

seq2seq_singal_prediction_tensorflow-

Tensorflow implementation seq2seq singal prediction

simulated-unsupervised-tensorflow

In progress

srez

Image super-resolution through deep learning

tensorflow

Computation using data flow graphs for scalable machine learning