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/

STAT479: Deep Learning (Spring 2019)
Instructor: Sebastian Raschka

Course material for STAT 479: Deep Learning (SS 2019) at University Wisconsin-Madison

https://github.com/rasbt/stat479-deep-learning-ss19

PyTorch prerequisites - Neural network programming series
http://deeplizard.com/learn/video/v5cngxo4mIg

Optimizing Steering Car Paths with PyTorch
http://blog.benwiener.com/programming/2019/05/14/steering-car.html

The mathematics of machine learning and deep learning

https://www.youtube.com/watch?v=r07Sofj_puQ

Handtrack.js: Hand Tracking Interactions in the Browser using Tensorflow.js and 3 lines of code
Blog by Victor Dibia: https://towardsdatascience.com/handtrackjs-677c29c1d585
#JavaScript #MachineLearning #ArtificialIntelligence #TensorFlow #TensorFlowJS

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

Speech2Face: Learning the Face Behind a Voice.

By MIT CSAIL: https://arxiv.org/pdf/1905.09773.pdf

"The term “artificial intelligence” dates back to the mid-1950s, when mathematician John McCarthy, widely recognized as the father of AI, used it to describe machines that do things people might call intelligent. He and Marvin Minsky, whose work was just as influential in the AI field, organized the Dartmouth Summer Research Project on Artificial Intelligence in 1956. A few years later, with McCarthy on the faculty, MIT founded its Artificial Intelligence Project, later the AI Lab. It merged with the Laboratory for Computer Science (LCS) in 2003 and was renamed the Computer Science and Artificial Intelligence Laboratory, or CSAIL."


https://www.the-scientist.com/magazine-issue/artificial-intelligence-versus-neural-networks-65802

Limitations of Deep Learning for Vision, and How We Might Fix Them by Alan L. Yuille, Chenxi Liu: https://thegradient.pub/the-limitations-of-visual-deep-lea…/

The most serious challenge is how to develop algorithms that can deal with the combinatorial explosion as researchers address increasingly complex visual tasks in increasingly realistic conditions.