dkosm

Adversarial_Video_Generation

A TensorFlow Implementation of "Deep Multi-Scale Video Prediction Beyond Mean Square Error" by Mathieu, Couprie & LeCun.

bcs-lstm

c-rnn-gan

Implementation of C-RNN-GAN.

ganhacks

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

Generative-Adversarial-Network-Tutorial

Tutorial on creating your own GAN in Tensorflow

gitignore

A collection of useful .gitignore templates

GRAN

hello-world

Tutorial Repo

models

Models built with TensorFlow

pandas

Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more

dkosm/Adversarial_Video_Generation

A TensorFlow Implementation of "Deep Multi-Scale Video Prediction Beyond Mean Square Error" by Mathieu, Couprie & LeCun.

dkosm/bcs-lstm

dkosm/c-rnn-gan

Implementation of C-RNN-GAN.

dkosm/ganhacks

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

dkosm/Generative-Adversarial-Network-Tutorial

Tutorial on creating your own GAN in Tensorflow

dkosm/gitignore

A collection of useful .gitignore templates

dkosm/GRAN

dkosm/hello-world

Tutorial Repo

dkosm/models

Models built with TensorFlow

dkosm/pandas

Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more

dkosm/Spoon-Knife

This repo is for demonstration purposes only.

dkosm/tensorflow

Computation using data flow graphs for scalable machine learning

dkosm/tensorflow-examples

Some examples to go along with the tensorflow workshop series.

dkosm/TensorFlow-Examples-1

TensorFlow Tutorial and Examples for Beginners with Latest APIs

dkosm/tensorflow-talk-debugging

💬 Slides and supplementary codes for my talk 'Debugging Tips on TensorFlow' (2016)

dkosm/tf-workshop-series

Code, Slides, & Materials for our Tensorflow Workshop Series

dkosm/tftables

HDF5 interface for Tensorflow.

dkosm/torchgeo

TorchGeo: datasets, samplers, transforms, and pre-trained models for geospatial data