Creative Applications of Deep Learning with TensorFlow
Project description
# Introduction
This package is part of the Kadenze Academy program [Creative Applications of Deep Learning w/ TensorFlow](https://www.kadenze.com/programs/creative-applications-of-deep-learning-with-tensorflow).
# Contents
This package contains various models, architectures, and building blocks covered in the Kadenze Academy program including:
Autoencoders
Character Level Recurrent Neural Network (CharRNN)
Conditional Pixel CNN
CycleGAN
Deep Convolutional Generative Adversarial Networks (DCGAN)
Deep Dream
Deep Recurrent Attentive Writer (DRAW)
Gated Convolution
Generative Adversarial Networks (GAN)
Global Vector Embeddings (GloVe)
Illustration2Vec
Inception
Mixture Density Networks (MDN)
PixelCNN
NSynth
Residual Networks
Sequence2Seqeuence (Seq2Seq) w/ Attention (both bucketed and dynamic rnn variants available)
Style Net
Variational Autoencoders (VAE)
Variational Autoencoding Generative Adversarial Networks (VAEGAN)
Video Style Net
VGG16
WaveNet / Fast WaveNet Generation w/ Queues / WaveNet Autoencoder (NSynth)
Word2Vec
and more. It also includes various datasets, preprocessing, batch generators, input pipelines, and plenty more for datasets such as:
CELEB
CIFAR
Cornell
MNIST
TedLium
LibriSpeech
VCTK
and plenty of utilities for working with images, GIFs, sound (wave) files, MIDI, video, text, TensorFlow, TensorBoard, and their graphs.
Examples of each module’s use can be found in the tests folder.
# Contributing
Contributions, such as other model architectures, bug fixes, dataset handling, etc… are welcome and should be filed on the GitHub.
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