Typical data scientist/machine learning engineer:
> understands that data science is generally engineering most of programmers can deal with
> do want to be overpaid and thus to keep others away from field
> keeps saying rare useless skill he possesses is definitively necessary for data science

Only truth, whole truth and nothing but the truth.

Towards reconstructing intelligible speech from the human auditory cortex



Columbia neuroengineers have created a system that translates thought into intelligible, recognizable speech. By monitoring someone's brain activity, the technology can reconstruct the words a person hears with unprecedented clarity.

Link: https://www.nature.com/articles/s41598-018-37359-z

#BCI #thought2text

​​#vizualization #statistics

​​StNet: Local and Global Spatial-Temporal Modeling for Action Recognition

Baidu in one of #AAAI19 papers, proposed StNet, a novel framework for both local and global spatial-temporal modeling in videos. StNet outperforms several state-of-the-art approaches in action recognition and balances btw accuracy and model complexity.

ArXiV: https://arxiv.org/pdf/1811.01549.pdf

#SOTA #Baidu

​​DeepSlide: A Sliding Window Framework for Classification of High Resolution Microscopy Images (Whole-Slide Images)

DeepSlide, our #DeepLearning library for classification/visualization of high-resolution pathology images is open-source and available on GitHub.

GitHub: https://github.com/BMIRDS/deepslide

#histopathology #healthcare #PyTorch

​​Introducing AresDB: Uber’s GPU-Powered Open Source, Real-time Analytics Engine

Link: https://eng.uber.com/aresdb/

#Uber #analytics #opensource

​​Fair Regression for Health Care Spending

What happens, if fairness built into the objective function for continuous outcomes & see large improvements in group undercompensation?

This is the most interesting & potentially impactful analysis of fairness in #ML for #healthcare, which can lead to significant improvement in the life of millions.

ArXiV: https://arxiv.org/abs/1901.10566
GitHub: https://github.com/zinka88/Fair-Regression

#statistics #regression

Using Nucleus and TensorFlow for DNA Sequencing Error Correction

This is a new #tutorial of the Genomics team in #GoogleBrain. Good place to start with #deeplearning for #genomics using Nucleus and #Tensorflow.

Link: https://medium.com/tensorflow/using-nucleus-and-tensorflow-for-dna-sequencing-error-correction-47f3f7fc1a50
Google colab: https://colab.research.google.com/github/google/nucleus/blob/master/nucleus/examples/dna_sequencing_error_correction.ipynb

#Google #healthcare

​​Bayesian Statistics explained to Beginners in Simple English

Now some #entrylevel material, which still might be useful to review, because repetitio est mater studiorum.

Link: https://www.analyticsvidhya.com/blog/2016/06/bayesian-statistics-beginners-simple-english/

State-of-the-art (SOTA) collection of Paperswithcode

A great site, worth spreading word about: Papers With Code now includes 950+ ML tasks, 500+ evaluation tables (including SOTA results) and 8500+ papers with code. Probably the largest collection of NLP tasks, including 140+ tasks and 100 datasets.

Link: https://paperswithcode.com
Sota link: https://paperswithcode.com/sota

#Meta #collection #sota #useful

Manifold: A Model-Agnostic Visual Debugging Tool for Machine Learning at Uber

Seesm like there is no week without any news from #Uber engineering team. This time Uber built Manifold, a model-agnostic visualization tool for #ML performance diagnosis and model debugging, to facilitate a more informed and actionable model iteration process.

Link: https://ubere.ng/2Hac0O8

#Pipeline #administration

​​Implementation of character based convolutional neural network

A #PyTorch implementation of Character Based ConvNets for text classification published by Yan LeCun in 2015 is open-sourced on. Many training features and hacks are implemented.

Link: https://github.com/ahmedbesbes/character-based-cnn

EE-559 – DEEP LEARNING (SPRING 2019)

Deep learning course covering the main deep learning tools and theoretical results, with examples in the #PyTorch framework.

Taught by François Fleuret from École Polytechnique Fédérale de Lausanne, Switzerland.

Link: https://fleuret.org/ee559/

#DL #course #learnhardgopro

​​Neural Networks seem to follow a puzzlingly simple strategy to classify images

Interesting article on how actually #NN see images and what helps to distinct different classes.

Link: https://medium.com/bethgelab/neural-networks-seem-to-follow-a-puzzlingly-simple-strategy-to-classify-images-f4229317261f

#BagNet #ResNet #Dl #CV

What do you get if you apply Data Science to Boxing ?

Been waiting for a long time to post something like this. Meet: BoxingBot, a robot, which uses #CV and various motion detectors to evade punches. It also can track statistics of hits. This is how #DL and #ML can improve any area of our lives, including such classy and old school, like boxing.

​​Machine Learning-Powered Search Ranking of Airbnb Experiences.

Post on how #AirBnB DS team built custom search, including notes on how they approached problem and what business results they achived.

Link: https://medium.com/airbnb-engineering/machine-learning-powered-search-ranking-of-airbnb-experiences-110b4b1a0789

#ranking #search #reallifeds #production

Text-based game to feel yourself in not so distant future where real AI exists.

We all now, how DS and #AI products affect our life and how they actually influence our lifestyle. But in the future our relationship will become more tense. It is rather nice experience to play this game, not only because it reminds of old-school games, but also because it looks shookingly possible.

Link: https://www.theverge.com/2019/1/31/18140796/wake-word-algorithm-text-game-ai-artificial-intelligence

#interactive #Meta

A Lyapunov-based Approach to Safe Reinforcement Learning

Research that develops RL algorithms based on the concept of Lyapunov functions. This work represents a step toward applying RL to real-world problems.

ArXiV: https://arxiv.org/abs/1805.07708

#RL #Lyapunov #facebook