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Telegram-канал opendatascience - Data Science by ODS.ai 🦜

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First Telegram Data Science channel. Covering all technical and popular staff about anything related to Data Science: AI, Big Data, Machine Learning, Statistics, general Math and the applications of former. To reach editors contact: @haarrp

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Data Science by ODS.ai 🦜

#DeepLearning predicts when patients die with Average Precision 0.69 (that’s high).

Andrew Ng announced new project in his twitter: ML to help prioritize palliative (end-of-life) care. Model uses an 18-layer Deep Neural Network that inputs the EHR data of a patient, and outputs the probability of death in the next 3-12 months.

The trained model achieves an AUROC score of 0.93 and an Average Precision score of 0.69 on cross validation.

Site: https://stanfordmlgroup.github.io/projects/improving-palliative-care/
Arxiv: https://arxiv.org/abs/1711.06402

#project #DSinthewild #casestudy

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StarGAN — a novel and scalable approach that can perform image-to-image translations for multiple domains using only a single model.

GitHub: https://github.com/yunjey/StarGAN
Arxiv: https://arxiv.org/abs/1711.09020

#deeplearning #gan #cv

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Data Science by ODS.ai 🦜

Realtime object detection by Google.

https://research.googleblog.com/2017/11/automl-for-large-scale-image.html

YouTube demo: https://www.youtube.com/watch?time_continue=70&v=ERglPgx8wFg

#deeplearning #google #caption #detection

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Data Science by ODS.ai 🦜

An article about #BigBrother. How Facebook is able to track users interests based on 3 likes.

Enhancing Transparency and Control When Drawing Data-Driven Inferences About Individuals

http://online.liebertpub.com/doi/full/10.1089/big.2017.0074

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Data Science by ODS.ai 🦜

#CapsNet #architecture

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Data Science by ODS.ai 🦜

Imitation learning for structured prediction in natural language processing

https://sheffieldnlp.github.io/ImitationLearningTutorialEACL2017

#nlp #tutorial

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Data Science by ODS.ai 🦜

Release of a nice NLP-processing library.

https://www.techleer.com/articles/404-spacy-20-released-natural-language-processing-with-python/

#nlp #python

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Data Science by ODS.ai 🦜

Great example of feature visualisation

https://distill.pub/2017/feature-visualization/

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ARkit Sudoku solver built with CoreML

https://blog.prototypr.io/behind-the-magic-how-we-built-the-arkit-sudoku-solver-e586e5b685b0

#ar #cv #keras #coreml

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Another breakthrough with generative models.

BEGAN: Boundary Equilibrium Generative Adversarial Networks

https://arxiv.org/abs/1703.10717

#gan #cv

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Netflix shared its recommendation engine scheme:

https://medium.com/netflix-techblog/distributed-time-travel-for-feature-generation-389cccdd3907

#ml #rs #spark #hadoop

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Data Science by ODS.ai 🦜

Architecture for real-time scene annotation (BlitzNet)


http://thoth.inrialpes.fr/research/blitznet/

ArxiV: https://arxiv.org/abs/1708.02813
GitHub: https://github.com/dvornikita/blitznet

#ICCV #github #dl #video

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Data Science by ODS.ai 🦜

Beautiful thematic maps with ggplot2

https://timogrossenbacher.ch/2016/12/beautiful-thematic-maps-with-ggplot2-only/

#viz #ggplot #maps

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Tutorial on how to launch Jupyter Notebook in Google GPU cloud.

https://hackernoon.com/launch-a-gpu-backed-google-compute-engine-instance-and-setup-tensorflow-keras-and-jupyter-902369ed5272

#tutorial #jupyter #google

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Data Science by ODS.ai 🦜

STMVis - Visual Analysis for Recurrent Neural Networks

LSTMVis a visual analysis tool for recurrent neural networks with a focus on understanding these hidden state dynamics. The tool allows a user to select a hypothesis input range to focus on local state changes, to match these states changes to similar patterns in a large data set, and to align these results with structural annotations from their domain. We provide data for the tool to analyze specific hidden state properties on dataset containing nesting, phrase structure, and chord progressions, and demonstrate how the tool can be used to isolate patterns for further statistical analysis.

http://lstm.seas.harvard.edu/

#harvard #video #dl #rnn

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An article about the impossibility of intelligence explosion. There will be no singularity or significant breakthrough and humanity will die off becuase of sun explosion.

francois.chollet/the-impossibility-of-intelligence-explosion-5be4a9eda6ec" rel="nofollow">https://medium.com/@francois.chollet/the-impossibility-of-intelligence-explosion-5be4a9eda6ec

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Astonishing results on emotion generation and image altering with StarGAN

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#CapsNet #tutorial on the YouTube

https://www.youtube.com/watch?v=pPN8d0E3900

#deeplearning

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Data Science by ODS.ai 🦜

And another posts on #CapsNet and how they work.

Capsule Networks Are Shaking up AI — Here’s How to Use Them: https://hackernoon.com/capsule-networks-are-shaking-up-ai-heres-how-to-use-them-c233a0971952

Understanding Hinton’s Capsule Networks. Part I: Intuition:
pechyonkin/understanding-hintons-capsule-networks-part-i-intuition-b4b559d1159b" rel="nofollow">https://medium.com/@pechyonkin/understanding-hintons-capsule-networks-part-i-intuition-b4b559d1159b

Understanding Hinton’s Capsule Networks. Part II: How Capsules Work:
pechyonkin/understanding-hintons-capsule-networks-part-ii-how-capsules-work-153b6ade9f66" rel="nofollow">https://medium.com/@pechyonkin/understanding-hintons-capsule-networks-part-ii-how-capsules-work-153b6ade9f66

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Data Science by ODS.ai 🦜

On 1st of November Geoff Hinton — one of the top NN researches has published two papers introducing new approach for #CV problems: Capsule Networks.

These architecture allows to recognize a face on the picture by detecting eyes, nose, mouth, regardless of the position / scaling / rotating the elements.

In other words, these approach allows neural network to be invariant to transformation of object.


First of papers: https://arxiv.org/abs/1710.09829
Second paper: https://openreview.net/forum?id=HJWLfGWRb&noteId=HJWLfGWRb

Article on Wired: https://www.wired.com/story/googles-ai-wizard-unveils-a-new-twist-on-neural-networks/

Explanation on hackernoon: https://hackernoon.com/what-is-a-capsnet-or-capsule-network-2bfbe48769cc

Another post with explanation: https://kndrck.co/posts/capsule_networks_explained/

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Google's open source candy for all ML community:

Source-to-Source Debuggable Derivatives


https://opensource.googleblog.com/2017/11/tangent-source-to-source-debuggable.html?m=1

#opensource #nn #python #google

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The State of Data Science & Machine Learning 2017 by Kaggle.

Very informative article about age, job titles, most popular languages and everything related to DS / ML.

Not to mention that source data is included.

https://www.kaggle.com/surveys/2017

#kaggle #statistics

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Winning approaches for solving Advanced Driver Assistance System challenge on Kaggle:

https://blog.getnexar.com/how-a-22-year-old-from-shanghai-won-a-global-deep-learning-challenge-76f2299446a1

#deeplearning #kaggle #cv

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https://twitter.com/pleonard/status/914671877146206208

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NLP for beginners

http://blog.kaggle.com/2017/08/25/data-science-101-getting-started-in-nlp-tokenization-tutorial/

#tutorial #nlp

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Comparison of 13 classic ML algorithms on 165 datasets.

https://arxiv.org/pdf/1708.05070.pdf

#meta #arxiv #ml

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Neural net for removing copyright marks.

https://www.theverge.com/2017/8/18/16162108/google-research-algorithm-watermark-removal-photo-protection

#cv #dl #google

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Data Science by ODS.ai 🦜

2900 members, hoooray!

Thank you all for sharing posts, for suggesting content throught @opendatasciencebot. Hope that our channel is useful for you and your research / study / work.

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Data Science by ODS.ai 🦜

Tutorial on Building a Facial Recognition Pipeline with Deep Learning in Tensorflow
https://hackernoon.com/building-a-facial-recognition-pipeline-with-deep-learning-in-tensorflow-66e7645015b8

#facerecognition #dl #tensorflow #cv #tutorial

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Data Science by ODS.ai 🦜

Deep Bilateral Learning for Real-Time Image Enhancement

Video about image auto-enhancing with neural networks.

https://www.youtube.com/watch?v=GAe0qKKQY_I

#cv #dl #autoenhance #mit #youtube #video

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