Neural network visualization toolkit for keras. Contribute to raghakot/keras-vis development by creating an account on GitHub.

Sparse Variational Dropout, ICML 2017. Contribute to senya-ashukha/variational-dropout-sparsifies-dnn development by creating an account on GitHub.

Содержание Часть 1: Введение Часть 2: Manifold learning и скрытые ( latent ) переменные Часть 3: Вариационные автоэнкодеры ( VAE ) Часть 4: Conditional VAE Часть 5: GAN (Generative Adversarial...

How we beat the state of the art to build a real-life A.I. app.

This experiment lets you draw together with a recurrent neural network model called Sketch-RNN. We taught this neural net to draw by training it on millions of doodles collected from the Quick, Draw! game. Once you start drawing an object, Sketch-RNN will…

Neural Network Libraries by Sony is the open source software to make research, development and implementation of neural network more efficient.

Сериал HBO «Кремниевая долина» выпустил настоящее приложение ИИ, которое распознаёт хотдоги и не-хотдоги, как приложение в четвёртом эпизоде четвёртогого сезона (приложение сейчас доступно для...

Neural image captioning (NIC) implementation with Keras 2. - oarriaga/neural_image_captioning

Interaction-networks_tensorflow - Tensorflow Implementation of Interaction Networks for Learning about Objects, Relations and Physics

Similar to Wasserstein GAN,
this is another theory-motivated paper with neat
applications to deep learning. Once again, if you are looking for proof
details, you are better off reading the original paper. The goal
of this post is to give background and motivation.

[Project Resources] ICML 2017 Paper (Preprint): https://arxiv.org/abs/1703.06217 Poster: https://www.dropbox.com/s/svh5610fpfh7np1/drann-poster.pdf?dl=0 Soft...

tensorflow-cdcgan - A short Conditional DCGAN tensorflow implementation.

Check it out!

Deep learning is about creating machine learning models from compossible blocks, called layers. In this webinar you will learn convolutional neural network architectures for image classification.

This post is about finding the minimum and maximum eigenvalues and the corresponding eigenvectors of a matrix \(A\) using Projected Gradient Descent. There are several algorithms to find the eigenvalues of a given matrix (See Eigenvalue algorithms). Although…

In this post I provide a brief overview of the Generative Adversarial Networks paper and walk through some of the code.

We present Performance RNN, an LSTM-based recurrent neural network designed to model polyphonic music with expressive timing and dynamics. Here’s an example...