Collections of Papers & Code on Domain Adaptation

https://github.com/zhaoxin94/awsome-domain-adaptation

TensorFlow Dev Summit 2019

https://www.youtube.com/playlist?list=PLQY2H8rRoyvzoUYI26kHmKSJBedn3SQuB

"Introduction to Deep Learning" Course

Slides, course materials, demos, and implementations

https://chokkan.github.io/deeplearning/

Myia is a new differentiable programming language. It aims to support large scale high performance computations (e.g. linear algebra) and their gradients. The main application Myia aims to support is research in artificial intelligence, in particular deep learning algorithms.


https://github.com/mila-iqia/myia

LSTM Autoencoder for Extreme Rare Event Classification in Keras
Ranjan et al.: https://towardsdatascience.com/lstm-autoencoder-for-extreme-rare-event-classification-in-keras-ce209a224cfb
#DeepLearning #DataScience #ArtificialIntelligence #DataScience

"Wasserstein GAN"

Written by James Allingham: https://www.depthfirstlearning.com/2019/WassersteinGAN

#DeepLearning #GenerativeModels #GenerativeAdversarialNetworks

Microsoft launches a drag-and-drop machine learning tool

Article by Frederic Lardinois: https://techcrunch.com/2019/05/02/microsoft-launches-a-drag-and-drop-machine-learning-tool-and-hosted-jupyter-notebooks/

#ArtificialIntelligence #DeepLearning #MachineLearning

Datasheets for Datasets
Gebru et al.: https://arxiv.org/abs/1803.09010
#Databases #ArtificialIntelligence #AIEthics #Ethics #MachineLearning

Minicourse in Deep Learning with PyTorch
By Alfredo Canziani: https://github.com/Atcold/pytorch-Deep-Learning-Minicourse
#DeepLearning #MachineLearning #PyTorch

State of the art video editing - make any object in a video invisible!

Deep Flow-Guided Video Inpainting
paper: https://www.profillic.com/paper/arxiv:1905.02884

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

Canada is Failing in Applied AI Innovation

"Canada is failing in Applied AI innovation and the impact will be severe unless everyone leads in new and more powerful intentionality ways."

Article by Dr. Cindy Gordon: https://cata.ca/2019/ai-innovation-lagging/

#ArtificialIntelligence #Canada #Governance

Human-Centered Tools for Coping with Imperfect Algorithms during Medical Decision-Making
Cai et al.: https://arxiv.org/abs/1902.02960
#humancentered #machinelearning #medical #innovation #technology

DL app that turns UI screenshots into a Bootstrap implementation. https://news.developer.nvidia.com/ai-turns-ui-designs-into…/

The code is open-source! https://github.com/tonybeltramelli/pix2code

Neural Stochastic Differential Equations: Deep Latent Gaussian Models in the Diffusion Limit
Belinda Tzen and Maxim Raginsky: https://arxiv.org/abs/1905.09883
#ArtificialIntelligence #DifferentialEquation #MachineLearning

New "Simple Self Attention" Layer
GitHub by Sébastien Doria: https://github.com/sdoria/SimpleSelfAttention
#MachineLearning #Pytorch #FastAI #SelfAttention

An Algorithmic Barrier to Neural Circuit Understanding
Venkatakrishnan Ramaswamy: https://www.biorxiv.org/content/10.1101/639724v1
#Algorithme #Neuroscience #innovation #technology

ArviZ: Exploratory analysis of Bayesian models
Includes functions for posterior analysis, sample diagnostics, model checking, and comparison: https://arviz-devs.github.io/arviz/
#ArtificialIntelligence #Bayesian #BayesianInference #MachineLearning #Python

VAE-SBD
PyTorch implementation of the Variational Autoencoder with Spatial Broadcast Decoder.
GitHub by Daniel Daza: https://github.com/dfdazac/vaesbd
#deeplearning #pytorch #technology #innovation

A curated list of gradient boosting research papers with implementations.

https://github.com/benedekrozemberczki/awesome-gradient-boosting-papers

How to Perform Object Detection With YOLOv3 in Keras
https://machinelearningmastery.com/how-to-perform-object-detection-with-yolov3-in-keras/