The rise in popularity and use of deep learning neural network techniques can be traced back to the innovations in the application of convolutional neural networks to image classification tasks.

Some of the most important innovations have sprung from submissions…

It's time to explore the world of bagging and boosting. With these powerful techniques, you can improve the performance of your models...

Bayesian optimization in PyTorch. Contribute to pytorch/botorch development by creating an account on GitHub.

Posted by Bingyi Cao and Tobias Weyand, Software Engineers, Google AI Last year we released Google-Landmarks, the largest world-wide landmark rec...

It is challenging to know how to best prepare image data when training a convolutional neural network. This involves both scaling the pixel values and use of image data augmentation techniques during both the training and evaluation of the model. Instead…

This paper presents a study of semi-supervised learning with large convolutional networks. We propose a pipeline, based on a teacher/student paradigm, that leverages a large collection of...

Специалистам по распознаванию речи давно не хватало большого открытого корпуса устной русской речи, поэтому только крупные компании могли позволить себе заниматься этой задачей, но они не...

Written by Óscar D. Lara Yejas and Ankit Jha

В этот раз выбрана игра «Змейка». Создана библиотека для нейросети на языке Go. Найден принцип обучения, зависимый от «глубины» памяти. Написан сервер для игр...

Posted by Vittorio Ferrari, Research Scientist, Machine Perception In 2016, we introduced Open Images, a collaborative release of ~9 million imag...

Achieve high text classification accuracy with smaller datasets.

Per-pixel ground-truth depth data is challenging to acquire at scale. To overcome this limitation, self-supervised learning has emerged as a promising alternative for training models to perform...