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…

A Generative Adversarial Network, or GAN, is a type of neural network architecture for generative modeling. Generative modeling involves using a model to generate new examples that plausibly come from an existing distribution of samples, such as generating…

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…

Generative Adversarial Networks, or GANs, are challenging to train.

This is because the architecture involves both a generator and a discriminator model that compete in a zero-sum game. It means that improvements to one model come at the cost of a degrading…

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...

The 2019 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) kicked off today in Long Beach, California. CVPR is one of the world’s top three academic conferences in the field of computer vision (along with ICCV and ECCV). A total of 1300 papers…

Adapters obtain comparable results to BERT on several NLP tasks while achieving parameter efficiency.