Good to watch
https://www.youtube.com/watch?v=Ioqrw4sCcwQ

Epileptic Seizure Classification ML Algorithms
https://towardsdatascience.com/seizure-classification-d0bb92d19962

Keras: Feature extraction on large datasets with Deep Learning
https://www.pyimagesearch.com/2019/05/27/keras-feature-extraction-on-large-datasets-with-deep-learning/

A curated list of gradient boosting research papers with implementations.

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

Week 2 CS294-158 Deep Unsupervised Learning (2/6/19)

#UCBerkeley #deeplearning #computervision

https://m.youtube.com/watch?v=mYCLVPRy2nc&feature=share

ML algorithms and their math -
1. Naive Bayes - https://goo.gl/m3gh1o
2. Decision Trees (ID3) - https://goo.gl/HFqAd4
3. Random Forest - https://goo.gl/y3Au8M
4. K-means - https://goo.gl/worGWg
5. Ridge Regression - https://goo.gl/YGdUFr
6. Logistic Regression - https://goo.gl/zDvRcF

https://www.thelearningmachine.ai/ml

AI-GAs: AI-generating algorithms, an alternate paradigm for producing general artificial intelligence
Jeff Clune: https://arxiv.org/abs/1905.10985
#ArtificialIntelligence #ArtificialGeneralIntelligence #AGI

Understanding Hinton’s Capsule Networks. Part I: Intuition.
Blog by Max Pechyonkin: https://medium.com/ai³-theory-practice-business/understanding-hintons-capsule-networks-part-i-intuition-b4b559d1159b
#MachineLearning #DeepLearning #GeoffreyHinton #ArtificialIntelligence #Theory

"End-to-End Deep Reinforcement Learning without Reward Engineering"
They developed an end-to-end method that allows robots to learn from a modest number of images that depict successful completion of a task.

https://bair.berkeley.edu/blog/2019/05/28/end-to-end/

"Fair is Better than Sensational:Man is to Doctor as Woman is to Doctor"
Do word embeddings really say that man is to doctor as woman is to nurse? Apparently not!
Nissim et al.: https://arxiv.org/abs/1905.09866
#ArtificialIntelligence #MachineLearning #NLProc #bias

How to deliver on Machine Learning projects
A guide to the ML Engineering Loop.
By Emmanuel Ameisen and Adam Coates: https://blog.insightdatascience.com/how-to-deliver-on-machine-learning-projects-c8d82ce642b0
#ArtificialIntelligence #BigData #DataScience #DeepLearning #MachineLearning

Open-ended learning in symmetric zero-sum games
Balduzzi et al.: https://proceedings.mlr.press/v97/balduzzi19a.html
#ArtificialIntelligence #MachineLearning #Nash #GameTheory

Utterance-level Aggregation for Speaker Recognition in the Wild
Project page https://www.robots.ox.ac.uk/~vgg/research/speakerID/
paper https://arxiv.org/pdf/1902.10107.pdf

Convex Optimization: Algorithms and Complexity
Sébastien Bubeck

Theory Group, Microsoft Research
[email protected]
https://arxiv.org/pdf/1405.4980.pdf

A new paper on learned lossless compression: 30% smaller images than PNG, using a fully parallel probabilistic model that is orders of magnitude faster than PixelCNN
PyTorch Implementation of the CVPR'19 Paper "Practical Full Resolution Learned Lossless Image Compression"
https://github.com/fab-jul/L3C-PyTorch

Very interesting paper from Google Research. Generating video from first and end frames
https://arxiv.org/pdf/1905.10240.pdf

Classification Accuracy Score for Conditional Generative Models
Suman Ravuri and Oriol Vinyals: https://arxiv.org/abs/1905.10887
#ArtificialIntelligence #DeepLearning #MachineLearning

Came out a few days back: MNIST test set with extra 50,000 training samples!
If your models overfit the original set, you should try it on this one! ;)
https://www.profillic.com/paper/arxiv:1905.10498

https://www.infoq.com/news/2019/05/google-tensorflow-graphics/ Google AI

Chinese AI Talent in Six Charts
Blog by Matt Sheehan: https://macropolo.org/china-ai-research-talent-data/
#ArtificialIntelligence #China #DeepLearning

Advanced NLP with spaCy by Ines Montani, core developers of spaCy: https://course.spacy.io

In this course, you'll learn how to use spaCy to build advanced natural language understanding systems, using both rule-based and machine learning approaches.