Deep learning cheatsheets, covering content of Stanford’s CS 230 class.

CNN: https://stanford.edu/~shervine/teaching/cs-230/cheatsheet-convolutional-neural-networks
RNN: https://stanford.edu/~shervine/teaching/cs-230/cheatsheet-recurrent-neural-networks
TipsAndTricks: https://stanford.edu/~shervine/teaching/cs-230/cheatsheet-deep-learning-tips-and-tricks

#cheatsheet #Stanford #dl #cnn #rnn #tipsntricks

​​Overview of current state of autonomously driving vehicle by Ben Evans.

Not so technical overview of where first autonomous vehicles will become commodity.

Link: https://www.ben-evans.com/benedictevans/2018/3/26/steps-to-autonomy

​​Creating super slow motion videos by predicting missing frames using a neural network, instead of simple interpolation. With code.

Github: https://github.com/avinashpaliwal/Super-SloMo
Website: https://people.cs.umass.edu/~hzjiang/projects/superslomo/

🎈🎊Geoff Hinton was named Companion of the Order of Canada (highest Canadian honor).

Hinton — one of the pioneers of the Deep Leaning, author of the famous «Neural Networks» on Coursera, one of the creators of the modern ML industry.

Congratulations!

https://www.gg.ca/en/media/news/2018/governor-general-announces-103-new-appointments-order-canada

​​Scaling Uber’s Apache Hadoop Distributed File System for Growth

Post on how #Uber team handles #Hadoop challenges.

https://eng.uber.com/scaling-hdfs/

#BigData #HDFS

​​Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond

New SOTA on cross-lingual transfer (XNLI, MLDoc) and bitext mining (BUCC) using a shared encoder for 93 languages.

Link: https://arxiv.org/abs/1812.10464

#SOTA #NLP

​​Building Automated Feature Rollouts on Robust Regression Analysis

Nice article on important thing — statistical analysis of hypothesis testing. Every new feature or change made to existent one is basically an experiment. Article covers how #Uber team handles this in live system.

Link: https://eng.uber.com/autonomous-rollouts-regression-analysis/

#Uber #statistics #production #truestory

Battling Entropy: Making Order of the Chaos in Our Lives

Article on #entropy as a concept.

Link: https://fs.blog/2018/11/entropy/

A disciplined approach to neural network hyper-parameters

Recommendations on how to optimize learning rate, weight decay, momentum and batch size.

ArXiV: https://arxiv.org/pdf/1803.09820.pdf

#nn #hyperopt

Top 10 IPython Notebook Tutorials for Data Science and Machine Learning

List mostly for beginners.

Link: https://www.kdnuggets.com/2016/04/top-10-ipython-nb-tutorials.html

#novice #beginner #ipython #jupyter

Tomorrow (05 Jan) we are holding first offline meeting for this channel members and all the Data Scientists in Paris.

You are kindly welcome to come by Malongo café at 10:00 to chat, share experience and have a coffee with fellow data scientist if you are in Paris these days.

Rachael’s #cheatsheet on choosing #DS library.

A Deep Learning Model to Predict a Diagnosis of Alzheimer Disease by Using 18F-FDG PET of the Brain

Computer Vision can detect Alzheimer’s Disease in brain scans SIX YEARS before a diagnosis. Uses PET scans, which are common & cheaper. 82% specificity at 100% sensitivity. Can pick out signs hard to see with the naked eye.

Link: https://pubs.rsna.org/doi/10.1148/radiol.2018180958

#CV #DL #Alzheimer #medical

​​Automatically Generating Comments for Arbitrary Source Code

Automatically generating code comments directly from source code using an LSTM. Works with multiple languages. Can’t wait to JetBrains discovering it.

Link: https://www.twosixlabs.com/automatically-generating-comments-for-arbitrary-source-code/

#NLP #CS #coding #LSTM

Data Science Breakfast in Paris. Thanks again for coming, hopefully this will setup a regular DS Breakfast in Paris.

​​Generalization in Deep Networks: The Role of Distance from Initialization

Why it's important to take into account the initialization to explain generalization.

ArXiV: https://arxiv.org/abs/1901.01672

#DL #NN