ArtificialIntelligenceArticles
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for who have a passion for -
1. #ArtificialIntelligence
2. Machine Learning
3. Deep Learning
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CS224N: Natural Language Processing with Deep Learning
CS230: Deep Learning
CS234: Reinforcement Learning
CS224U: Natural Language Understanding

https://onlinehub.stanford.edu/
"Linear Algebra"

Instructor : Prof. Gilbert Strang

Video Lectures (Support for the video production was provided by the Lord Foundation of Massachusetts under a grant to the MIT Center for Advanced Educational Services) : https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/video-lectures/

#LinearAlgebra #Matrices #MachineLearning
Meta-Learning with Warped Gradient Descent. https://arxiv.org/abs/1909.00025
Fast and Accurate Network Embeddings via Very Sparse Random Projection. https://arxiv.org/abs/1908.11512
Supervised learning using backpropagation is limited in terms of applications and efficacy. Many new AI approaches attempt to move beyond this traditional algorithm for training neural networks



https://www.topbots.com/deeper-than-deep-learning-beyond-backpropagation-geoffrey-hinton/
This man spent 20 years hammering away at his idea
Most of AI superstars has learned from him https://www.youtube.com/watch?v=UTfQwTuri8Y&feature=share
Happy birthday to the late John McCarthy, father of AI & LISP:

https://bit.ly/2omV4Y1
PracticalAI

A practical approach to learning machine learning

GitHub : https://github.com/GokuMohandas/practicalAI

- 📚 Notebooks on topics from basic Python to advanced deep learning techniques w/ #PyTorch

- 🖥️ Run everything using #Colab : https://colab.research.google.com/github/GokuMohandas/practicalAI/

#deeplearning #python #machinelearning #reinforcementlearning
Pathologies of Factorised Gaussian and MC Dropout Posteriors in Bayesian Neural Networks
Foong et al.: https://arxiv.org/abs/1909.00719
#Bayesian #NeuralNetworks #MachineLearning
A single-layer RNN can approximate stacked and bidirectional RNNs, and topologies in betw... https://arxiv.org/abs/1909.00021
Creating a data set and a challenge for deepfakes
https://ai.facebook.com/blog/deepfake-detection-challenge/