Deriving Differential Target Propagation from Iterating Approximate Inverses
Yoshua Bengio : https://arxiv.org/abs/2007.15139
#ArtificialIntelligence #DeepLearning #MachineLearning

Analyses of Deep Learning (STATS 385)
Stanford University, Fall 2019 : https://stats385.github.io/lecture_videos
#ArtificialIntelligence #DeepLearning #MachineLearning

List of Open-Source RL Algorithms
By Sergey Kolesnikov, https://docs.google.com/spreadsheets/d/1EeFPd-XIQ3mq_9snTlAZSsFY7Hbnmd7P5bbT8LPuMn0/edit#gid=0
#DeepLearning #MachineLearning #ReinforcementLearning

"Papers with Code": https://paperswithcode.com
#DeepLearning #MachineLearning #NeuralNetworks

The Deep Learning Lecture Series
A collaboration between DeepMind and the UCL Centre for Artificial Intelligence : https://www.youtube.com/playlist?list=PLqYmG7hTraZCDxZ44o4p3N5Anz3lLRVZF
#ArtificialIntelligence #DeepLearning #ReinforcementLearning

Can Single Neurons Solve MNIST? The Computational Power of Biological Dendritic Trees
Ilenna Simone Jones, Konrad Paul Kording : https://arxiv.org/abs/2009.01269
#Neurons #DeepLearning #Cognition

MiniTorch
Sasha Rush and Ge Gao : https://minitorch.github.io/index.html
#DeepLearning #PyTorch #Python

Deep Reinforcement Learning
CS 285 at UC Berkeley, Fall 2020
Levine et al.: https://www.youtube.com/playlist?list=PL_iWQOsE6TfURIIhCrlt-wj9ByIVpbfGc
#ArtificialIntelligence #DeepLearning #ReinforcementLearning

Efficient Transformers: A Survey
Tay et al.: https://arxiv.org/abs/2009.06732
#Transformer #DeepLearning #ArtificialIntelligence

Dive into Deep Learning with this machine learning course taught by industry veterans.

You'll learn about Random Forests, Gradient Descent, Recurrent Neural Networks, and other key coding concepts. All you need to get started with this course is some Python knowledge and a little high school math. And if you need to brush up on those, freeCodeCamp has you covered. (15 hour YouTube course):

#deeplearning

https://www.freecodecamp.org/news/learn-deep-learning-from-the-president-of-kaggle/