Beautiful thematic maps with ggplot2

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

#viz #ggplot #maps

Neural net for removing copyright marks.

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

#cv #dl #google

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

Comparison of 13 classic ML algorithms on 165 datasets.

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

#meta #arxiv #ml

Netflix shared its recommendation engine scheme:

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

#ml #rs #spark #hadoop

NLP for beginners

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

#tutorial #nlp

Another breakthrough with generative models.

BEGAN: Boundary Equilibrium Generative Adversarial Networks

https://arxiv.org/abs/1703.10717

#gan #cv

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

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

Great example of feature visualisation

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

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

Release of a nice NLP-processing library.

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

#nlp #python

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

Imitation learning for structured prediction in natural language processing

https://sheffieldnlp.github.io/ImitationLearningTutorialEACL2017

#nlp #tutorial

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/

#CapsNet #architecture

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:
https://medium.com/@pechyonkin/understanding-hintons-capsule-networks-part-i-intuition-b4b559d1159b

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

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