@DeepLearningHub

Account for Deep Learning related news, papers, software, reading materials and also other machine learning related news and facts

plus.google.com/107322247125350038821
Joined November 2014
  • Tweets2.3K
  • Following411
  • Followers51.1K
35Media
Deep Learning Hub retweeted
Dr Darshana Nadkarni@DarshanaN3 Apr 2018
Tomorrow Tue 4/3 talk on "Deep Learning & its application in Medicine" at 456 W. Olive Avenue, Sunnyvale. This is a free event & no pre-registration is required. Walk-ins are welcome. Come for talk, stay to network @machinelearnbot @MachineLearnDC @DeepLearningHub @deeplearning4j – at Fremont, CA
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Deep Learning Hub retweeted
Sebastian Risi
@risi197931 Mar 2018
Deadline to apply for postdoc position in deep continual learning is coming up tomorrow. @slashML @women_in_ai @wimlds @DeepLearningHub xitter.cf/risi1979/status/965598…
Sebastian Risi
@risi197919 Feb 2018
Postdoc position in machine learning. Especially looking for candidates with experience in memory networks/neural turing machine/synaptic plasticity/neuroevolution/deep RL. Please RT/forward to interested candidates. Thanks! candidate.hr-manager.net/App…
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Jeremy Howard
@jeremyphoward9 Jan 2018
Replying to @yoavgo @nlpmattg @seb_ruder @Smerity @dennybritz
A good example of a paper that pulls together quite a few useful techniques is: openreview.net/forum?id=S1NH… . CIFAR 10 SoTA, which is an interesting dataset IMO since it is quite small, so needs good augmentation, regularization, architecture, etc
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Denny Britz@dennybritz9 Jan 2018
[1801.01586] A practical tutorial on autoencoders for nonlinear feature fusion: Taxonomy, models, software and guidelines arxiv.org/abs/1801.01586 This looks like a very useful survey/tutorial paper.

A practical tutorial on autoencoders for nonlinear feature fusion:...

Many of the existing machine learning algorithms, both supervised and unsupervised, depend on the quality of the input characteristics to generate a good model. The amount of these variables is...

arxiv.org
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Deep Learning Hub retweeted
Maciej Kula@Maciej_Kula9 Jan 2018
Wrote a blog post on implementing a PyTorch-like autodifferentiation library (in @rustlang): medium.com/@maciejkula/build…

Building an autodifferentiation library

Popular general-purpose auto-differentiation frameworks like PyTorch or TensorFlow are very capable, and, for the most part, there is…

medium.com
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Deep Learning Hub retweeted
Rishabh Shukla@0_rishabh7 Jan 2018
Introduction to Linguistics for Natural Language Processing - cl.cam.ac.uk/teaching/1314/L… #NLProc #Linguistics
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François Chollet
@fchollet4 Jan 2018
The ML research community has long been driven by the need to publish, which results in a stark, sometimes ridiculous bias towards complexity. Remember to ask: "can we do this with k-means and logistic regression?"
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agibsonccc@agibsonccc5 Jan 2018
One thing I realize people don't like: Being told to think critically about a problem.People: AI algorithms are not magic. When you're first learning them, take some time to consider the knobs you're turning. It's amazing what applying the scientific method (step by step ) does.
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DSME.Foundation@DSME161 Jan 2018
How to unit test #machinelearning code . medium.com/@keeper6928/how-t…

How to unit test machine learning code.

Edit: The popularity of this post has inspired me to write a machine learning test library. Go check it out!

thenerdstation.medium.com
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Deep Learning Hub retweeted
Andrew Mauboussin
@amaub3 Jan 2018
A review of the top 5 papers ML arXiv papers of 2017 (according to the number of likes each paper's @BrundageBot tweet received):
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New: My research w/@andyguess/@JasonReifler providing 1st behavioral estimates of fake news exposure in 2016 dartmouth.edu/~nyhan/fake-ne… Key findings: -heavily concentrated among w/most conservative info diets -Facebook key vector of exposure -fact-checks did not reach those exposed
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Deep Learning Hub retweeted
Gary Marcus
@GaryMarcus3 Jan 2018
Top 10 reasons #deeplearning isn’t getting us to artificial general intelligence. A critique of deep learning, 5 years into its resurgence, by @garymarcus arxiv.org/abs/1801.00631

Deep Learning: A Critical Appraisal

Although deep learning has historical roots going back decades, neither the term "deep learning" nor the approach was popular just over five years ago, when the field was reignited by papers such...

arxiv.org
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Deep Learning Hub retweeted
François Chollet
@fchollet2 Jan 2018
My publisher is doing a deal (today only) where you can get half off my book "Deep Learning with Python", as well as its R version, "Deep Learning with R" (co-authored with JJ Allaire) manning.com/dotd?a_aid=keras

Deal of the Day

Manning is an independent publisher of computer books, videos, and courses.

manning.com
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Deep Learning Hub retweeted
Richard Socher
@RichardSocher21 Dec 2017
A reinforcement learning agent that learns to program new neural network architectures. Same/better results as LSTMs but with funky nonlinearities (sine, SeLus, etc) and new connections that result in different activation patterns😯 einstein.ai/research/domain-… arxiv.org/abs/1712.07316
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Yann LeCun
@ylecun29 Dec 2017
New results on word embeddings with FastText in this paper: arxiv.org/abs/1712.09405 fb.me/3qcGSgz3k
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Deep Learning Hub retweeted
Jeremy Howard
@jeremyphoward2 Jan 2018
In depth discussion of neural nets for autonomous driving and visualization of layers - Byte magazine, 1989 xitter.cf/ankurhandos/status/948…
Ankur Handa
@ankurhandos2 Jan 2018
"What's hidden in the hidden layers?" cs.cmu.edu/~dst/pubs/byte-hi…
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Deep Learning Hub retweeted
Yann LeCun
@ylecun31 Dec 2017
wav2letter: end-to-end speech recognition engine from FAIR, now open source. fb.me/1KE0VqEtS
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Deep Learning Hub retweeted
Yann LeCun
@ylecun29 Dec 2017
SLAC: A new video dataset for action recognition, brought to you by a team from Facebook Applied Machine Learning... fb.me/CetjVaFy
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Deep Learning Hub retweeted
Gregory Renard@Redo29 Nov 2017
[1711.09534] Neural Text Generation: A Practical Guide buff.ly/2ih3PE3
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Deep Learning Hub retweeted
Awni Hannun
@awnihannun29 Nov 2017
A gold mine of practical tips for generating text with encoder-decoder models arxiv.org/abs/1711.09534. From my lab-mate Ziang Xie. Includes strategies for: -Debugging training and decoding -Overly short sentences and repeating in outputs -Lack of output diversity

Neural Text Generation: A Practical Guide

Deep learning methods have recently achieved great empirical success on machine translation, dialogue response generation, summarization, and other text generation tasks. At a high level, the...

arxiv.org
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