Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!! - kmario23/deep-learning-drizzle

Posted by Abhinav Rastogi, Software Engineer and Pranav Khaitan, Engineering Lead, Google Research Today's virtual assistants help users to accom...

Self-supervision provides effective representations for downstream tasks
without requiring labels. However, existing approaches lag behind fully
supervised training and are often not thought...

A guest post by Thomas Wolf and Lysandre Debut from Hugging Face. All examples used in this tutorial are available on Colab. The links are…

Maximum likelihood estimation is an approach to density estimation for a dataset by searching across probability distributions and their parameters. It is a general and effective approach that underlies many machine learning algorithms, although it requires…

Posted by Aakanksha Chowdhery, Software Engineer

Monte Carlo methods are a class of techniques for randomly sampling a probability distribution.

There are many problem domains where describing or estimating the probability distribution is relatively straightforward, but calculating a desired quantity…

We present Joey NMT, a minimalist neural machine translation toolkit based on
PyTorch that is specifically designed for novices. Joey NMT provides many
popular NMT features in a small and simple...

To make deliberate progress towards more intelligent and more human-like artificial systems, we need to be following an appropriate feedback signal: we need to be able to define and evaluate...

This project reproduces the book Dive Into Deep Learning (https://d2l.ai/), adapting the code from MXNet into PyTorch. - dsgiitr/d2l-pytorch

Density estimation is the problem of estimating the probability distribution for a sample of observations from a problem domain.

Typically, estimating the entire distribution is intractable, and instead, we are happy to have the expected value of the distribution…

This repository presents UR-FUNNY dataset: first dataset for multimodal humor detection - GitHub - ROC-HCI/UR-FUNNY: This repository presents UR-FUNNY dataset: first dataset for multimodal humor...

The computations required for deep learning research have been doubling every few months, resulting in an estimated 300,000x increase from 2012 to 2018 [2]. These computations have a surprisingly...

Developing machine learning models in Python often requires the use of NumPy arrays.

NumPy arrays are efficient data structures for working with data in Python, and machine learning models like those in the scikit-learn library, and deep learning models…

We demonstrate the possibility of what we call sparse learning: accelerated training of deep neural networks that maintain sparse weights throughout training while achieving dense performance...