Ian Goodfellow (Google Brain) will have a free webinar on Adversarial Machine Learning

https://event.on24.com/eventRegistration/EventLobbyServlet?target=reg20.jsp&partnerref=twitterShareFromReg&ms=1531251848082&eventid=1633807&sessionid=1&key=8B7A8F4B65B54C35752F8A6FE23F641A&regTag=&sourcepage=register

#webinar #google #gbrain

Very thoroughful notes on #ICML2018

https://david-abel.github.io/blog/posts/misc/icml_2018.pdf

Learning World Models: The Next Step Towards AI

Video of Yann LeCun's keynote speech. First-hand expectations of closeness of real AI. Spoiler: still not even near yet.

https://www.facebook.com/722677142/posts/10155402036352143/

Takeaways from Netflix’s Personalization Workshop 2018

https://medium.com/rtl-tech/my-takeaways-from-netflixs-personalization-workshop-2018-f564a19437b6

#recommendation #netflix #summary

A cool paper from Facebook AI (not from FAIR!) about detecting and reading text in images, at scale. 

This is very useful for detecting inappropriate content on Facebook.

The system uses R-CNN/Detectron for detecting lines of text.

The OCR uses a ConvNet applied at the level of a whole line trained with CTC.

This concept of applying a ConvNet on a whole line of text, without prior segmentation, has roots in the early days of ConvNets, for example with this NIPS 1992 paper:
"Multi-Digit Recognition Using a Space Displacement Neural Network"
by Ofer Matan, Chris Burges, Yann LeCun and John Denker.

Link: https://papers.nips.cc/paper/557-multi-digit-recognition-using-a-space-displacement-neural-network
Youtuve video with short explanation: https://youtu.be/yl3P2tYewVg

#ocr #cv #dl #rnn #facebook #yannlecun #video

Cancer metastasis detection with neural conditional random field (NCRF)

Github: https://github.com/baidu-research/NCRF?utm_source=telegram&utm_medium=opendatascience

#Baidu #Cancer #Segmentation #cv #DL

XGBoost deployment made easy

Rather obscure way to serve XGBoost models, but still works.

Link: https://towardsdatascience.com/xgboost-deployment-made-easy-6e11f4b3f817

#production #xgboost #deployment

Series of articles «Machine Learning Kaggle Competition» in three parts:

1. Part I: https://towardsdatascience.com/machine-learning-kaggle-competition-part-one-getting-started-32fb9ff47426
2. Part II: https://towardsdatascience.com/machine-learning-kaggle-competition-part-two-improving-e5b4d61ab4b8
3. Part III: https://towardsdatascience.com/machine-learning-kaggle-competition-part-three-optimization-db04ea415507

#kaggle #guide

friend: yo dude, wanna drink?

me: nah man, that stuff kills brain cells.

friend: you say its killing brain cells but i say its just real life dropout to prevent overfitting

OpenAI bot defeated 5 human top 99.95 percentile DotA 2 players.

https://twitch.tv/openai

#dota #rl #openai

Complete code examples for Machine Translation with Attention, Image Captioning, Text Generation, and DCGAN implemented with tf.keras and eager execution

“Complete code examples for Machine Translation with Attention, Image Captioning, Text Generation…” https://medium.com/tensorflow/complete-code-examples-for-machine-translation-with-attention-image-captioning-text-generation-51663d07a63d

#tensorflow #tutorial

27.23TB of research data in torrents! Includes dataset such as:
- Breast Cancer Cell Segmentation
- Liver Tumor Segmentation
- MRI Lesion Segmentation in Multiple Sclerosis
- Electron Microscopy, Hippocampus
- Digital Surface & Digital Terrain Model

And courses recordings, including:
- Introduction to Computer Science [CS50x] [Harvard] [2018]
- Artificial Intelligence(EDX)
- Richard Feynman's Lectures on Physics (The Messenger Lectures) (🔥)
- [Coursera] Machine Learning (Stanford University) (ml)
- [Coursera] Natural Language Processing (Stanford University) (nlp)
- [Coursera] Neural Networks for Machine Learning (University of Toronto) (neuralnets)

http://academictorrents.com/

#course #torrent #dataset

Kaggle kernel on classifing Russian Troll tweets.

https://www.kaggle.com/kmader/from-hate-speech-to-russian-bot-tweets

#kaggle #kernel #nlp

Teams at #DeepMind and #Moorfields have developed AI technology that can detect eye disease and prioritise patients. 'Clinically applicable deep learning for diagnosis and referral in retinal OCT' has been published online in #NatureMedicine today:
https://www.nature.com/articles/s41591-018-0107-6

#cv #dl

Fast image-to-image translation in the browser. With 3 trained models introduced. Plus a processed dataset of 1000 images for edges2cats translation.

Demo: https://zaidalyafeai.github.io/pix2pix/cats.html
Code: https://github.com/zaidalyafeai/zaidalyafeai.github.io/tree/master/pix2pix

#tf #tensorflow #tfjs #pix2pix #cv

«IEEE’s camera identification challenge — different approach to teaming up»

https://hackernoon.com/ieees-camera-identification-challenge-different-approach-to-teaming-up-28da44dfe635

#cv #dbrain

Neural nets are terrible at arithmetic & counting. If you train one in 1 to 10, it will do okay on 3 + 5 but fail miserably for 1000 + 3000. Resolving this, «Neural Arithmetic Logic Units» can track time, do arithmetic on images of numbers, & extrapolate, providing better results than other architectures.

https://arxiv.org/pdf/1808.00508.pdf

#nn #architecture #concept #deepmind #arithmetic

The Conversational Intelligence Challenge 2 (ConvAI second part) got announced today.

The aim of our competition is to establish a concrete scenario for testing chatbots that aim to engage humans, and become a standard evaluation tool in order to make such systems directly comparable.

The most promising team attending will receive a travel grant to attend #NIPS2018

#nlp #dl #dialoguesystem #competition