The blind application of machine learning runs the risk of amplifying biases present in data. Such a danger is facing us with word embedding, a popular framework to represent text data as vectors...

Word & Sentence Embeddings have evolved really fast over the last few months - A brief primer on what happened

A free course designed for people with some coding experience, who want to learn how to apply deep learning and machine learning to practical problems.

Researchers from NVIDIA developed a deep learning-based system that can produce high-quality slow-motion videos from a 30-frame-per-second video, outperforming various state-of-the-art methods that aim to do the same.

Learn more: https://nvda.ws/2Mr2ZPt…

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Both convolutional and recurrent operations are building blocks that process one local neighborhood at a time. In this paper, we present non-local operations as a generic family of building blocks...

Retro Games in Gym. Contribute to openai/retro development by creating an account on GitHub.

Lecture notes for Stanford cs228.

A code tutorial in Tensorflow that uses Reinforcement Learning to take free kicks.

As powerful and widespread as convolutional neural networks are in deep learning, AI Labs’ latest research reveals both an underappreciated failing and a simple fix.

What if you could take your photos that were originally taken in low light and automatically remove the noise and artifacts? Have grainy or pixelated images in your photo library and want to fix them?

What a time to be alive!

The paper "Example-based Turbulence Style Transfer" is available here: http://nishitalab.org/user/syuhei/TurbuStyleTrans/turbu_styletrans.html

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Welcome to the 17th Issue of the NLP Newsletter! Here is this week's notable NLP news: Deep contextualized representations (ELMo), new IDE…