A must read document for deep learning & machine learning practitioners

https://www.deeplearningbook.org/contents/guidelines.html
#deep_learning #machine_learning

A fascinating research paper in the intersection of Graph Neural Networks and Reinforcement Learning for tackling Robotics challenges

https://openreview.net/pdf?id=S1sqHMZCb
#robotics #deep_learning #geometric_deep_learning

Noam Chomsky: Language, Cognition, and Deep Learning | Artificial Intelligence

Noam Chomsky is one of the greatest minds of our time and is one of the most cited scholars in history. He is a linguist, philosopher, cognitive scientist, historian, social critic, and political activist. He has spent over 60 years at MIT and recently also joined the University of Arizona. This conversation is part of the Artificial Intelligence podcast.

https://www.youtube.com/watch?v=cMscNuSUy0I
#natural_language_processing #deep_learning

Dive into Deep Learning (D2L Book)

Dive into Deep Learning: an interactive deep learning book with code, math, and discussions, based on the NumPy interface

https://github.com/d2l-ai/d2l-en
#deep_learning

An Overview of Recent State of the Art Deep Learning Algorithms/Architectures

Lecture on most recent research and developments in deep learning, and hopes for 2020. This is not intended to be a list of SOTA benchmark results, but rather a set of highlights of machine learning and AI innovations and progress in academia, industry, and society in general. This lecture is part of the MIT Deep Learning Lecture Series.

https://www.youtube.com/watch?v=0VH1Lim8gL8&t=999s
#deep_learning #artificial_intelligence

Deep Reasoning Papers

A repository which contains recent papers including Neural Symbolic Reasoning, Logical Reasoning, Visual Reasoning, natural language reasoning and any other topics connecting deep learning and reasoning.

https://github.com/floodsung/Deep-Reasoning-Papers
#reasoning #deep_learning #artificial_intelligence

An overview of gradient descent optimization algorithms

Abstract: Gradient descent optimization algorithms, while increasingly popular, are often used as black-box optimizers, as practical explanations of their strengths and weaknesses are hard to come by. This article aims to provide the reader with intuitions with regard to the behaviour of different algorithms that will allow her to put them to use. In the course of this overview, we look at different variants of gradient descent, summarize challenges, introduce the most common optimization algorithms, review architectures in a parallel and distributed setting, and investigate additional strategies for optimizing gradient descent

https://arxiv.org/pdf/1609.04747.pdf
#deep_learning #optimization

A cool 3D representation of the structure of BERT language model

Blogpost: https://peltarion.com/knowledge-center/documentation/modeling-view/build-an-ai-model/blocks/bert-encoder
#NLP #deep_learning