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Bharath proslijedio/la je Tweet
CausalNex – Python library to infer causation rather than observing correlation By
@QuantumBlack (@bnhorsburgh, @ntsaouthe) Github: https://github.com/quantumblacklabs/causalnex … Medium: https://medium.com/@QuantumBlack/introducing-causalnex-driving-models-which-respect-cause-and-effect-a561545f0a5e …pic.twitter.com/Sd3YPaXwFD
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Bharath proslijedio/la je Tweet
Had a great time talking about T5 and chatting with students
@NYUDataScience yesterday! I'm waiting to put slides online until I finish annotating them; in the meantime here is a recording of the same talk from when I gave it@allen_ai earlier this month: https://www.youtube.com/watch?v=eKqWC577WlI …https://twitter.com/sleepinyourhat/status/1222553470827405312 …
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This was by far the most difficult (and interesting)
#MachineLearning#DeepLearning#NLP post I have worked on. Tokenization is a really exciting field of research in and of itself. Loved working with@FloydHub_ to get it publishedhttps://blog.floydhub.com/tokenization-nlp/ …Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
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We tie 6 strongly simplified RNN layers in a CPU-optimized transformer hybrid with very little quality loss. Been doing that for two years now in production, wrote about it only now :)https://www.aclweb.org/anthology/D19-5632/ …
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I have 8 free copies of ML Bookcamp To get one: - Follow me - Retweet this tweet ML Bookcamp: a project-based way to learn ML. Get a portfolio and the skills to work as a data scientist or ML engineer https://www.manning.com/books/machine-learning-bookcamp …
@ManningBooks@ManningMEAP Results on WednesdayHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
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I teach a general ML course with minimal deep learning targeted toward biomedical grad students (not CS), and I teach EM. They struggle, but latent variables are important if you want to do any causal/mechanistic analysis.
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Bharath proslijedio/la je Tweet
Transformers are great at modeling sequences, but the attention layer requires L^2 memory, where L is the length of the sequence. Nikita Kitaev,
@lukaszkaiser, and Anselm Levskaya show how to decrease memory footprint. Very promising for biological data! https://arxiv.org/abs/2001.04451 pic.twitter.com/zf5X9342cH
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Bharath proslijedio/la je Tweet
PS - The prettier DNS for the explorer is now available here: http://cui2vec.dbmi.hms.harvard.edu
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Current image datasets are getting very large and pre-training on them is time consuming. We are releasing Neural Data Server (NDS), a search engine for transfer learning data!
@yanxi0830@davidjesusacu#UofT Webservice: http://aidemos.cs.toronto.edu/nds Paper: https://arxiv.org/abs/2001.02799 pic.twitter.com/i5od82gekdHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
I like this new coinage of term " Cognitive automation " rather than "Artificial Intelligence "https://twitter.com/fchollet/status/1214392496375025664 …
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I think when any field grows and becomes lucrative in terms of money and fame, all the wicked behaviors of human nature come out. ML, DL, AI are in gold rush mode. Such things are not unexpected.https://twitter.com/abhi1thakur/status/1211892526547951616 …
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Seeing
@kaggle since 2013, it seems there was way less hatred and jealousy and people used to share and help others learn. Now it's all about attacking others. These days kagglers try to pick points from other's lifes to bully them. No wonder why so many legends retired.Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
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Episode1: Building a machine learning framework. Premiers in 10hours!!!!https://youtu.be/ArygUBY0QXw
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sandesh (संदेश) in Hindi means message. This is a simple python library to send messages to Slack using webhook urls. You can install this using "pip install sandesh" Check it out here:https://github.com/abhishekkrthakur/sandesh …
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Bharath proslijedio/la je Tweet
Do you have a core intuition behind dimension reduction?
@leland_mcinnes gives a great guide to understand most dimension reduction techniques:
Matrix factorization or,
Neighbour graph
https://bit.ly/35alhgP
He will be with us at #AITopology at@appliedmldayspic.twitter.com/7w51nOYQcw
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Bharath proslijedio/la je Tweet
As promised, we have made the Text-To-Text Transfer Transformer (T5) models much easier to fine-tune for new tasks, and we just released a Colab notebook where you can try it yourself on a free TPU!
http://tiny.cc/t5-colab
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Human Learning is not achieved by chance, it must be sought with zeal & worked with diligence. Evolution doesn’t have this flavor on the surface, since it’s concerned with “only” survival. Until you realize how vicious nature can be, also demanding zeal & diligence. Poor
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Unsupervised Sentiment Analysis by Rafał Wójcikhttps://buff.ly/33kDyGE
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Bernhard Scholkopf (
@bschoelkopf) just published a single author paper titled "Causality for Machine Learning" (https://arxiv.org/abs/1911.10500 ); this should probably at the top of the reading list for many people interested in machine learning / AI;@yudapearl@eliasbareinboimPrikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Bharath proslijedio/la je Tweet
"Neural Random Forest Imitation," Reinders and Rosenhahn: https://arxiv.org/abs/1911.10829 "Without any additional training data, this transformation [RF --> data --> NN] creates very efficient neural networks that learn the decision boundaries of a random forest."
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