Deep Learning Hub
@DeepLearningHub
Account for Deep Learning related news, papers, software, reading materials and also other machine learning related news and facts
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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
Deadline to apply for postdoc position in deep continual learning is coming up tomorrow. @slashML @women_in_ai @wimlds @DeepLearningHub
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/ApplicationIni…
A good example of a paper that pulls together quite a few useful techniques is: openreview.net/forum?id=S1NHa… . CIFAR 10 SoTA, which is an interesting dataset IMO since it is quite small, so needs good augmentation, regularization, architecture, etc
[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.
Wrote a blog post on implementing a PyTorch-like autodifferentiation library (in @rustlang): medium.com/@maciejkula/bu…
Introduction to Linguistics for Natural Language Processing - cl.cam.ac.uk/teaching/1314/… #NLProc #Linguistics
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?"
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.
A review of the top 5 papers ML arXiv papers of 2017 (according to the number of likes each paper's @BrundageBot tweet received):
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
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
“From Perceptron to Deep Neural Nets” by @adichrisb buff.ly/2DVAp3v
ICYMI...
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=ker…
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/domai… arxiv.org/abs/1712.07316
New results on word embeddings with FastText in this paper: arxiv.org/abs/1712.09405 fb.me/3qcGSgz3k
In depth discussion of neural nets for autonomous driving and visualization of layers - Byte magazine, 1989
"What's hidden in the hidden layers?" cs.cmu.edu/~dst/pubs/byte…
wav2letter: end-to-end speech recognition engine from FAIR, now open source. fb.me/1KE0VqEtS
A nice summary...
I wrote up a (not so brief) summary of AI developments that stood out to me in 2017 wildml.com/2017/12/ai-and…
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