‘Cortex’: An open-source platform for deploying machine learning models as production web services
Github: https://github.com/cortexlabs/cortex
Tutorial: https://www.cortex.dev/iris-classifier
Examples: https://github.com/cortexlabs/cortex/tree/0.11/examples
https://www.marktechpost.com/2019/12/23/cortex-an-open-source-platform-for-deploying-machine-learning-models-as-production-web-services/

State of the art in deblurring and generating realistic high-resolution facial images
https://www.profillic.com/paper/arxiv:1912.10427

(An adversarial network comprising a generator and two discriminators is proposed)

Two Postdoc positions (m/f/d) in ‘Computational proteomics/deep learning’ and ‘Digital pathology/proteomics’
https://www.nature.com/naturecareers/job/two-postdoc-positions-mfd-in-computational-proteomicsdeep-learning-and-digital-pathologyproteomics-max-planck-institute-of-biochemistry-715743

https://t.iss.one/ArtificialIntelligenceArticles

Macaw: An Extensible Conversational Information Seeking Platform. https://arxiv.org/abs/1912.08904

Pruning by Explaining: A Novel Criterion for Deep Neural Network Pruning. https://arxiv.org/abs/1912.08881

Yann lecun : energy based self supervised learning

https://www.youtube.com/watch?v=A7AnCvYDQrU


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@ArtificialIntelligenceArticles

Videos of talks from #BlackinAI and #NeurIPS 2019, in case you missed it!

https://slideslive.com/neurips/neurips-2019-east-meeting-room-1-3-live?fbclid=IwAR0tEjCRVwRP6lXTYJNkNMHyffmyxlhot8X9LVi8fE6apTnIEMb1Wk77vwM

Shtetl-Optimized (entertaining & informative blog by theoretical computer scientist Scott Aaronson). NIPS vs. NeurIPS: guest post by Steven Pinker https://www.scottaaronson.com/blog/?p=4476

Best Machine Learning Research of 2019 https://opendatascience.com/best-machine-learning-research-of-2019/

Andrew Ng Interview with Father Of Deep Learning , Geoffrey Hinton |Andrew Ng and Geoffrey Hinton https://www.youtube.com/watch?v=089U2maCg0Q&feature=youtu.be&app=desktop

Workshop on Theory of Deep Learning: Where Next? Playlist

If you want to learn more about the current state of the art in the theory of deep learning, then these presentations are a great starting point. They feature talks from luminaries such as Sanjeev Arora, Chris Manning, and Anima Anandkumar, among many others. https://www.youtube.com/playlist?list=PLdDZb3TwJPZ5dqqg_S-rgJqSFeH4DQqFQ&utm_campaign=NLP+News&utm_medium=email&utm_source=Revue+newsletter

Deep Learning Pioneers Yann LeCun and Yoshua Bengio Elected as AAAI-20 Fellows
https://techgrabyte.com/deep-learning-yann-lecun-yoshua-bengio-aaai/

Tesla's Neural Net can now identify red and green traffic lights, garbage cans, and detailed road markings

https://www.teslarati.com/tesla-holiday-update-fsd-preview-neural-net-improvements/

As projects move from small-scale research to large-scale deployment, managing resources becomes a daunting task. In this article, we will look at some universal #bestpractices to achieve successful #deeplearning model rollout for a company of any size and means.
https://nanonets.com/blog/10-best-practices-deep-learning/

The AI technology we're developing is designed to prioritise patients who need to be seen and treated urgently by a doctor or eye care professional. If we can diagnose and treat eye conditions early, it gives us the best chance of saving people's sight. With further research it could lead to greater consistency and quality of care for patients with eye problems in the future."

"Using two types of neural network - mathematical systems for identifying patterns in images or data - the AI system quickly learnt to identify 10 features of eye disease from highly complex optical coherence tomography (OCT) scans. The system was then able to recommend a referral decision based on the most urgent conditions detected

https://www.ucl.ac.uk/news/2018/aug/artificial-intelligence-equal-experts-detecting-eye-diseases?

Starting January 6, we're doing a series of lectures at MIT on deep learning and AI. Skip the first one, but afterwards there are some great talks. All are welcome. Seating limited (first come, first served). Video will be posted a couple days after each lecture. Details: https://
https://deeplearning.mit.edu/