Deep learning sequence models have led to a marked increase in performance
for a range of Natural Language Processing tasks, but it remains an open
question whether they are able to induce proper...

We develop a generalisation of disentanglement in variational autoencoders (VAEs)—decomposition of the latent representation—characterising it as the fulfilm...

Generative Adversarial Networks, or GANs, are a type of deep learning technique for generative modeling. GANs are the techniques behind the startlingly photorealistic generation of human faces, as well as impressive image translation tasks such as photo colorization…

In this guide we will build a deep neural network, with as many layers as you want! The network can be applied to supervised learning…

Заголовок статьи может показаться странным и это неспроста — он прекрасен именно тем, что написал его не я, а LSTM-нейросеть (а точнее его часть перед "или"). (схема LSTM взята из Understanding LSTM...

An off-the-shelf robust classifier can be used to perform a range of computer vision tasks beyond classification.

Networks that can already (sort of) perform tasks with random weights.

Bayesian Deep Learning Benchmarks. Contribute to OATML/bdl-benchmarks development by creating an account on GitHub.

A collection of various deep learning architectures, models, and tips - rasbt/deeplearning-models

* Machine Learning Engineer Masters Program: https://www.edureka.co/masters-program/machine-learning-engineer-training *
Artificial Intelligence in Healthcare is revolutionizing the medical industry by providing a helping hand. This Edureka session will help…

Convolutions are a fundamental building block of modern computer vision systems. Recent approaches have argued for going beyond convolutions in order to capture long-range dependencies. These...