How to Code the GAN Training Algorithm and Loss Functions
https://machinelearningmastery.com/how-to-code-the-generative-adversarial-network-training-algorithm-and-loss-functions/

Deep learning based super resolution, without using a GAN
Blog by Christopher Thomas: https://towardsdatascience.com/deep-learning-based-super-resolution-without-using-a-gan-11c9bb5b6cd5
#MachineLearning #DeepLearning #ArtificialIntelligence

WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia
Schwenk et al.: https://arxiv.org/abs/1907.05791
Data: https://github.com/facebookresearch/LASER/tree/master/tasks/WikiMatrix
#artificialintelligence #bigdata #datascience #machinelearning

AI Habitat tensorboard integration
Watch videos of your agents in training!
Courtesy: Jason Jiazhi Zhang: https://github.com/facebookresearch/habitat-api/pull/127
Habitat-API, by Facebook Research, is a modular high-level library to train embodied AI agents across a variety of tasks, environments, and simulators: https://aihabitat.org
#ArtificielIntelligence #DeepLearning #ReinforcementLearning

NPA: Neural News Recommendation with Personalized Attention. arxiv.org/abs/1907.05559

New fast.ai course: A Code-First Introduction to Natural Language Processing

https://www.fast.ai/2019/07/08/fastai-nlp/

Github: https://github.com/fastai/course-nlp

What does it mean to understand a neural network? arxiv.org/abs/1907.06374

"HighRes-net for Multi-Frame Super-Resolution by Recursive Fusion"
Pytorch implementation of HighRes-net, a neural network trained and tested on the European Space Agency’s Kelvin competition. GitHub, by ElementAI AI for Good lab and Mila: https://github.com/ElementAI/HighRes-net
Blog, by ElementAI's Team AI for Good: https://www.elementai.com/news/2019/computer-enhance-please
#ai4good #neuralnetwork #pytorch

Presentation streaming live from https://Neuralink.com at 8pm Pacific

Calculus on Computational Graphs: Backpropagation
Blog by Christopher Olah: https://colah.github.io/posts/2015-08-Backprop/
#Calculus #ComputationalGraphs #Backpropagation

A Beginner’s Guide to Python for Data Science
https://towardsdatascience.com/a-beginners-guide-to-python-for-data-science-60ef022b7b67

Generative models are an extension of Richard Feynman's quote, "What I cannot create, I don't understand."

📃 Understanding deep learning requires rethinking generalization (paper ref from beginning of the talk):

https://openreview.net/pdf?id=Sy8gdB9xx

Efficient Video Generation on Complex Datasets
pdf: arxiv.org/pdf/1907.06571
abs: arxiv.org/abs/1907.06571

This year I wrote a book teaching Deep Learning - it's goal is to be the easiest intro possible

In the book, each lesson builds a neural component *from scratch* in #NumPy

Each *from scratch* toy code example is in the Github below. https://github.com/iamtrask/Grokking-Deep-Learning

The Bach Doodle: Approachable music composition with machine learning at scale. arxiv.org/abs/1907.06637

Cross-Lingual Transfer Learning for Question Answering. arxiv.org/abs/1907.06042