Where to start with Data Science

There is now way to be taught to be data scientist, but you can learn how to become one yourself. There is no right way, but there is a way, which was adopted by a number of data scientists and it goes through online courses (MOOC). Following suggested order is not required, but might be helpful.

Best resources to study Data Science /Machine Learning

1. Andrew Ng’s Machine Learning (https://www.coursera.org/learn/machine-learning).
2. Geoffrey Hinton’s Neural Networks for Machine Learning (https://www.coursera.org/learn/neural-networks).
3. Probabilistic Graphical Models specialisation on Coursera from Stanford (https://www.coursera.org/specializations/probabilistic-graphical-models).
4. Learning from data by Caltech (https://work.caltech.edu/telecourse.html).
5. CS229 from Stanford by Andrew Ng (http://cs229.stanford.edu/materials.html)
6. CS224d: Deep Learning for Natural Language Processing from Stanford (http://cs224d.stanford.edu/syllabus.html).
7. CS231n: Convolutional Neural Networks for Visual Recognition from Stanford (http://cs231n.stanford.edu/syllabus.html).
8. Deep Learning Book by Ian Goodfellow and Yoshua Bengio and Aaron Courville (http://www.deeplearningbook.org/).
9. Machine Learning Yearning by Andrew Ng (http://www.mlyearning.org/).

#books #wheretostart #mooc

Where to start with Data Science (list of ML / DL online courses and useful resources):

http://telegra.ph/Where-to-start-with-Data-Science-01-02

GAN architecture provides extremely live-looking generated pictures.

https://medium.com/@Moscow25/gans-will-change-the-world-7ed6ae8515ca#.pskqd0wjw

There is a new $1MM competition on Kaggle to use ML / AI to diagnose lung cancer from CT scans.

Not only it is the great breakthrough for Kaggle (it is the first competition with this huge prize fund), it is also a breakthrough for science, since top world researchers and enginners will compete to basically crowdsource and ease the lung cancer diagnostics.

Competition is available at: https://www.kaggle.com/c/data-science-bowl-2017

#kaggle #segmentation #deeplearning #cv

Trained agent was able to beat some humans in one-on-one no-limit Texas Holdem. It is not "beating human" yet.

https://www.technologyreview.com/s/603342/poker-is-the-latest-game-to-fold-against-artificial-intelligence/

New release in PyTorch: «GPU Tensors, Dynamic Neural Networks and deep Python integration. Hello world!»

http://pytorch.org

Deep Learning Pipeline for Alzheimer’s Disease Prediction

https://devblogs.nvidia.com/parallelforall/nvidia-digits-alzheimers-disease-prediction/

#deeplearning #digits

Image-to-Image Translation in Tensorflow

http://affinelayer.com/pix2pix/index.html

#deeplearning #tf #dl

Today Kaggle announced the launch of Two Sigma's new recruiting competition. In this competition, participants are invited to explore detailed NYC rental listing data from Two Sigma's competition co-sponsor, RentHop, to ease the often hectic process of finding the perfect home.

#kaggle

If you have any news worth spreading, please address @opendatasciencebot and send him a link with quick description.

Simple/limited/incomplete benchmark for scalability, speed and accuracy of machine learning libraries for classification.

https://github.com/szilard/benchm-ml

#github #opensource #worthspreading

2.72% on the CIFAR-10

https://github.com/xgastaldi/shake-shake

#github #cifar

Best probability visualisations ever. If you struggling with basic Probability Theory concepts, or just want to review them, this site is the best for understanding/refreshing.

http://students.brown.edu/seeing-theory/

https://www.paperspace.com/ml

Nice ML computing in the cloud

Another on-demand-GPU cloud solution:

http://floydhub.com

https://github.com/soulmachine/machine-learning-cheat-sheet

#github #cheatsheet

An article about using #cv to predict car manufacturer, political views and average household based on Google's Street View

https://arxiv.org/pdf/1702.06683.pdf