After election scandal, #facebook surges to fight fake news, and that’s how is it going now.
https://techcrunch.com/2018/09/13/photo-video-fact-checking/
#nlp #ai #fakenews
https://techcrunch.com/2018/09/13/photo-video-fact-checking/
#nlp #ai #fakenews
TechCrunch
Facebook rolls out photo/video fact checking so partners can train its AI
Sometimes fake news lives inside of Facebook as photos and videos designed to propel misinformation campaigns, instead of off-site on news articles that can generate their own ad revenue. To combat these politically rather than financially motivated meddlers…
NVIDIA's article on #AI training and validation infrastructure for autonomous driving with MagLev, introduced at #facebook event.
https://blogs.nvidia.com/blog/2018/09/13/how-maglev-speeds-autonomous-vehicles-to-superhuman-levels-of-safety/
#selfdriving #ci
https://blogs.nvidia.com/blog/2018/09/13/how-maglev-speeds-autonomous-vehicles-to-superhuman-levels-of-safety/
#selfdriving #ci
NVIDIA Blog
MagLev: Efficient AI Training for Safe Autonomous Vehicles | NVIDIA Blog
NVIDIA announced on Thursday at the Facebook @Scale Conference Project MagLev — an internal AI training and inference infrastructure.
How Booking.com increases the power of online A/B experiments with CUPED
Link: https://booking.ai/how-booking-com-increases-the-power-of-online-experiments-with-cuped-995d186fff1d
#ab #statistics #booking
Link: https://booking.ai/how-booking-com-increases-the-power-of-online-experiments-with-cuped-995d186fff1d
#ab #statistics #booking
Medium
How Booking.com increases the power of online experiments with CUPED
Simon Jackson |Data Scientist at Booking.com
#OpenDataScience community (ods.ai) recently released Open Machine Learning Course. This is a community-driven course, covering #production, #Kaggle (actually #CompetitiveDataScience, but we use this hashtag for the first time), #DL, #RL, #ML and validated on the russian-speaking DS community, which was translated into english.
There are lots of assignments and some competitions during the course. Interactive rating highly motivates and makes it fun to participate.
Next session starts on October 1. Welcome!
Link: https://mlcourse.ai?utm_source=telegram&utm_medium=opendatascience
There are lots of assignments and some competitions during the course. Interactive rating highly motivates and makes it fun to participate.
Next session starts on October 1. Welcome!
Link: https://mlcourse.ai?utm_source=telegram&utm_medium=opendatascience
#MIT recent release for video labeling
Youtube: https://www.youtube.com/watch?v=JBwSk6nJOyM&feature=youtu.be
Github: https://github.com/metalbubble/TRN-pytorch
#dl #video
Youtube: https://www.youtube.com/watch?v=JBwSk6nJOyM&feature=youtu.be
Github: https://github.com/metalbubble/TRN-pytorch
#dl #video
YouTube
How a Temporal Relation Network understands what's going on there
Prediction of the on-going activity from a TRN is shown. Yes, I am playing my hands :)
Model of TRN and code are available at https://github.com/metalbubble/TRN-pytorch
Model is trained on Something-Something-V2 dataset.
Model of TRN and code are available at https://github.com/metalbubble/TRN-pytorch
Model is trained on Something-Something-V2 dataset.
You can also suggest any news, repo or post to our channel with @opendatasciencebot
You are welcome to share your open source work to request help, or just ask for starring it.
You are welcome to propose any post or article you want to.
Channel editors however will decide if that follows channel policy, which is OK for the majority of subscribers, judging by recent poll.
Thank you for your patience and constant forwards to friends! You do really matter, dear subscribers and the organic growth of this channel is the best reward.
You are welcome to share your open source work to request help, or just ask for starring it.
You are welcome to propose any post or article you want to.
Channel editors however will decide if that follows channel policy, which is OK for the majority of subscribers, judging by recent poll.
Thank you for your patience and constant forwards to friends! You do really matter, dear subscribers and the organic growth of this channel is the best reward.
“Software 2.0” last year, but still great article about what might happen in the future with the Software Development.
https://medium.com/@karpathy/software-2-0-a64152b37c35
https://medium.com/@karpathy/software-2-0-a64152b37c35
Medium
Software 2.0
I sometimes see people refer to neural networks as just “another tool in your machine learning toolbox”. They have some pros and cons, they…
Imposter Syndrome is a condition when person feels a liar, no matter what his/her achievements are. It is a great deal in the #DS too.
https://caitlinhudon.com/2018/01/19/imposter-syndrome-in-data-science
https://caitlinhudon.com/2018/01/19/imposter-syndrome-in-data-science
Haystacks by Caitlin Hudon
Imposter Syndrome in Data Science — Haystacks by Caitlin Hudon
Thoughts on why imposter syndrome is so prevalent in data science, how I deal with it personally, and ways we can encourage people who are feeling the impact.
New library for #DataAugmentation: SOLT.
Supports various transformations for images, masks, targets and landmarks. Fast and easy-to-use library useful in #ComputerVision and #DeepLearning
Link: https://github.com/MIPT-Oulu/solt .
Supports various transformations for images, masks, targets and landmarks. Fast and easy-to-use library useful in #ComputerVision and #DeepLearning
Link: https://github.com/MIPT-Oulu/solt .
GitHub
GitHub - MIPT-Oulu/solt: Streaming over lightweight data transformations
Streaming over lightweight data transformations. Contribute to MIPT-Oulu/solt development by creating an account on GitHub.
Paper «A Probabilistic U-Net for Segmentation of Ambiguous Images» from #NIPS2018 spotlight presentation.
Github: https://github.com/SimonKohl/probabilistic_unet
Github: Arxiv: https://arxiv.org/abs/1806.05034
#DeepMind #segmentation #cv
Github: https://github.com/SimonKohl/probabilistic_unet
Github: Arxiv: https://arxiv.org/abs/1806.05034
#DeepMind #segmentation #cv
GitHub
GitHub - SimonKohl/probabilistic_unet: A U-Net combined with a variational auto-encoder that is able to learn conditional distributions…
A U-Net combined with a variational auto-encoder that is able to learn conditional distributions over semantic segmentations. - GitHub - SimonKohl/probabilistic_unet: A U-Net combined with a variat...
Deploying a Machine Learning Model as a REST API
Yet another #tutorial on how to serve #production model with flask.
Link: https://towardsdatascience.com/deploying-a-machine-learning-model-as-a-rest-api-4a03b865c166
Github repo: https://github.com/mnguyenngo/flask-rest-setup/tree/master/sentiment-clf
Yet another #tutorial on how to serve #production model with flask.
Link: https://towardsdatascience.com/deploying-a-machine-learning-model-as-a-rest-api-4a03b865c166
Github repo: https://github.com/mnguyenngo/flask-rest-setup/tree/master/sentiment-clf
Medium
Deploying a Machine Learning Model as a REST API
As a Python developer and data scientist, I have a desire to build web apps to showcase my work. As much as I like to design the front-end…
Deep learning application in classic and conservative area.
https://towardsdatascience.com/portfolio-optimization-with-deep-reinforcement-learning-2bcda07483b5
#moneytalking #dl #portfoliooptimization
https://towardsdatascience.com/portfolio-optimization-with-deep-reinforcement-learning-2bcda07483b5
#moneytalking #dl #portfoliooptimization
Hacker Noon
Optimizing a Portfolio of Cryptocurrencies with Deep Reinforcement Learning
Portfolio Optimization or the process of giving optimal weights to assets in a financial portfolio is a fundamental problem in Financial…
AI Poker beat humans for the first time in two simultaneous-ish projects: DeepStack & Libratus, both of which use Counterfactual Regret Minimization. Good intro post for CRM, starting from Game Theory basics:
Blog: https://int8.io/counterfactual-regret-minimization-for-poker-ai/
Code: https://github.com/int8/counterfactual-regret-minimization
#rl #dl #poker
Blog: https://int8.io/counterfactual-regret-minimization-for-poker-ai/
Code: https://github.com/int8/counterfactual-regret-minimization
#rl #dl #poker
Int8
Counterfactual Regret Minimization - the core of Poker AI beating professional players
The last 10 years have been full of unexpected advances in artificial intelligence. Among great improvements in image processing and speech recognition - the thing that got lots of media attention was AI winning against humans in various kinds of games. With…
Data Hackers is a brazilian data science community and we've just released the first episode of our podcast. They reached out with the @opendatasciencebot saying that there're a lot of brazilians in our channel and it would be great to share this Portugese message:
Lançamos o podcast do Data Hackers. O primeiro episódio já está disponível no Spotify, Google Podcasts, iTunes, e muitas outras plataformas de streaming. Você também pode ouvir no post abaixo, além de saber um pouco mais sobre o projeto.
https://medium.com/data-hackers/confira-o-podcast-de-data-science-e-machine-learning-do-data-hackers-28d4659843b7
Lançamos o podcast do Data Hackers. O primeiro episódio já está disponível no Spotify, Google Podcasts, iTunes, e muitas outras plataformas de streaming. Você também pode ouvir no post abaixo, além de saber um pouco mais sobre o projeto.
https://medium.com/data-hackers/confira-o-podcast-de-data-science-e-machine-learning-do-data-hackers-28d4659843b7
Medium
Confira o podcast de Data Science e Machine Learning do Data Hackers
Mais recente projeto da comunidade tem como objeto trazer dicas e levantar discussões sobre Ciência de Dados.
#GoogleAI ’s paper on highly-accurate genomes via #DeepLearning was published today in #NatureBiotech.
#DeepVariant is an analysis pipeline that uses a deep neural network to call genetic variants from next-generation DNA sequencing data.
Link: https://rdcu.be/7Dhl
Github: https://github.com/google/deepvariant
#DeepVariant is an analysis pipeline that uses a deep neural network to call genetic variants from next-generation DNA sequencing data.
Link: https://rdcu.be/7Dhl
Github: https://github.com/google/deepvariant
Nature
A universal SNP and small-indel variant caller using deep neural networks
Nature Biotechnology - DeepVariant uses convolutional neural networks to improve the accuracy of variant calling.
Resilient Computing with Reinforcement Learning on a Dynamical System: Case Study in Sorting
RL agent learnt to sort using significantly fewer array manipulations than Quicksort.
Arxiv: https://arxiv.org/abs/1809.09261
RL agent learnt to sort using significantly fewer array manipulations than Quicksort.
Arxiv: https://arxiv.org/abs/1809.09261
Brilliant post on #CS and #Software about strategy and psychology of Software Development, which is highly applicable to Data Science too.
“Imaginary Problems Are the Root of Bad Software”
https://medium.com/s/story/imaginary-problems-d4f2921bd1b8
“Imaginary Problems Are the Root of Bad Software”
https://medium.com/s/story/imaginary-problems-d4f2921bd1b8
Medium
Imaginary Problems Are the Root of Bad Software
Just because they're fun to solve doesn't mean they're relevant