Massive Speech Dataset !!! 19,000 hours of Apollo-11 recordings

TASK#1: Speech Activity Detection: SAD

TASK#2: Speaker Diarization: SD

TASK#3: Speaker Identification: SID

TASK#4: Automatic Speech Recognition: ASR

TASK#5: Sentiment Detection: SENTIMENT
http://fearlesssteps.exploreapollo.org/

#NASA #speech #sentiment #dataset

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A lecture on security and privacy in ML I recently gave is now available on YouTube. It covers a bunch of open problems in this space.

🌎 Link Review

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Article calls out a “looking where the light is” effect of deep neural networks. They drive us to focus on problems with large labeled data sets. The (many fascinating and important) problems that have little or no labels get neglected in comparison.
https://thegradient.pub/the-limitations-of-visual-deep-learning-and-how-we-might-fix-them/

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Dear Data Scientists. Please, do not get fooled by false claims that logistic regression is not a regression, if you don't want to fail interviews led by people with statistical background. So, let's clarify this briefly:

Regression is a statistical procedure for estimating the conditional expected response based on the relationship between DV and IVs. That fits the historic context of "regression towards the mean" by Galton. And that's exactly what LR does (recall what is the expectation here).

The logistic regression is an instance of the Generalized Linear Model with binomial response, namely it's the Binomial Regression. It has 2 cases, depending on the form of the link function: Logistic and Probit regression. Itself is does nothing with classification. Classification is an application of it, if a threshold is defined for the probability.

There is also ordinal and multinomial regression, for DV with more than 2 ordered and unordered categories, respectively. Both logit and probit link are used here too.

You may be interested in the history of the LR:
https://lnkd.in/gYChNvt


#DataScience #Statistics #RockYourR #MachineLearning

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Presenting 4 more true stories of professionals in our community transitioning into #DataScience from a variety of backgrounds:

How I became a Data Science Hacker from being a Delivery Head - https://lnkd.in/faQDP2p

How I became a Data Science #Analyst from a Software Developer - https://lnkd.in/fYueNbn

Becoming a #DataScientist after 8 Years as a Software Test Engineer - https://lnkd.in/fjihReg

I became a Data Scientist after working for 10 years in IT Industry - https://lnkd.in/fibY7iB

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THE UNIMPORTANCE OF RELATIVE IMPORTANCE

Kruskal and Majors (1989) somewhat sadly conclude that it was not sufficiently clear from the literature how people assess the relative importance of the different independent variables (or reasons) in accounting for an observed phenomenon. I think, however, that they have missed an important factor, which is that only unsophisticated people try to make such assessments. As soon as the relationships in question come to be better understood (or those investigating them become at all knowledgeable), the discussion turns, I think, to modelling the processes and their possible causal mechanisms as such, rather than their relative “importance.” I am reminded of the small boy coming home with a very bad school report but understanding the possible causes well enough: “Father!” he said, “Father! Do you think it is heredity or environment?”

A. S. C. Ehrenberg, Letter to the Editor, The American Statistician, August 1990

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The entire field of artificial intelligence, in the last few years, is built upon deep learning or deep neural networks. Notably, Apple’s Siri, Google-DeepMinds’ AlphaGo, or the self-driving mechanism in Tesla cars are all based on deep learning. Here, my goal is to make deep learning neural networks much more accessible for everyone.

https://lnkd.in/gvgPnXP

#ai #machinelearning #deeplearning

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#DataScience #statistics #MachineLearning
Happy (nerdy) Valentines Day
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Today we're sharing new features & research results related to ELF OpenGo, including an updated model that was retrained from scratch. We're also releasing a Windows executable version of the bot, and a unique archive analyzing 87K professional Go games.

https://ai.facebook.com/blog/open-sourcing-new-elf-opengo-bot-and-go-research/

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What’s the best way to learn and ingrain a concept? Learning the theory is a good start, but learning by doing is when we truly understand how that technique works. And that’s especially true of a field that’s as vast as deep learning.

There’s no shortage of datasets to hone your skills – but where should you start? Which datasets are the best at building your profile? And can you get domain specific datasets which will help you get acquainted with that line of work? To help you out, we scoured the internet and hand-picked the top 25 open deep learning datasets.

These datasets are divided into three categories:

Image Processing
Natural Language Processing
Audio/Speech Processing

So pick your interest and get started today!

25 Open Datasets for Deep Learning Every Data Scientist Must Work With

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Awesome news for beginners in #MachineLearning and #DeepLearning

We've all come to love Dr. Strang's Linear Algebra Lectures from MIT. But his books are sometimes expensive for students and also not available.

Now Stanford University changed all that with their free book they released called "Introduction to Applied Linear Algebra" written by Stephen Boyd and Lieven Vandenberghe

Go get them all here on my #Github page, I will create some beginners lectures and #Python & #Julia notebooks there soon.

Root / main folder: https://lnkd.in/de8uepd

1. The 473 page book itself: https://bit.ly/2tjFNdA
2. Lovely Julia language companion book worth 170 pages! : https://bit.ly/2BxYGy0
3. Exercises book: https://bit.ly/2RZoVTf
4, Course lecture slides: https://bit.ly/2N9TZPC

#linearalgebra #computing #DeepLearning

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Standardizing a machine learning framework for applied research: PyTorch vs. MXNet

https://www.borealisai.com/en/blog/standardizing-machine-learning-framework-applied-research/

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Microsoft has open sourced a collection of Jupyter Notebooks detailing best practices with machine learning and deep learning

https://github.com/Microsoft/Recommenders

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Is your heart beating fast for someone this Valentine’s Day? AI can detect that for you 💓💓💓

Awni Hannun, Pranav Rajpurkar et al trained a neural network to detect abnormal heart rhythms: https://lnkd.in/gAKrr6c

Free PDF here: https://lnkd.in/gEiVFXc

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What is the difference between SGD (Stochastic Gradient Descent) vs GD (Gradient Descent)?

We keep revisiting fundamentals every now and then and we encourage you to do the same.

Get the notebook here (but do please practice it yourself): https://lnkd.in/exAeCpB

#DeepLearning #fundamentals #MachineLearning

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An article by Aaron Bornstein about #nlp toolkits, explaining various features on each and provided a notebook so that we can start experimenting with them.
https://medium.com/microsoftazure/7-amazing-open-source-nlp-tools-to-try-with-notebooks-in-2019-c9eec058d9f1

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Large teams develop and small teams disrupt science and technology. Truly transformative research is done by small teams.
🌎 Link
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Vidhyalakshmi Sundara Raman
Let's say you open-source your MNIST-trained ConvNet and the Postal Service decides to use it to read zipcodes.
Since your ConvNet is open source, someone could make a malicious white-box attack that would imperceptibly modify every envelope so as to maximally confuse your ConvNet.
Swarms of painter-drones would attack mailboxes and carefully corrupt every zipcode.
Every piece of junk mail would end up in the wrong zipcode!
Surely, civilization would crumble as a result.
If I were you, I would buy a place on a remote island to survive the inevitable armageddon, like, right now.
#regulateMNIST

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HOW CMLD TRANSFORMING MACHINE LEARNING PROFESSIONSHOW CMLD TRANSFORMING MACHINE LEARNING PROFESSIONS
https://www.youtube.com/watch?v=OrQqtC1YSRI
IF INTERESTED https://www.bepec.in/cmld

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Techniques for Detection of Rusting of Metals using Image Processing: A Survey

Most of industries around us make use of iron machines & tools for manufacturing their products. On the other
hand corrosion is a natural process that deteriorates the integrity
of iron surface. Therefore, rusting of iron takes place. To avoid unwanted accidents in industries, it is necessary to detect rusting in earlier stage, so that it can be prevented. Digital Image Processing for the detection of the rusting provides fast, accurate and objectives results. There have been many techniques for detection of rust. In this paper we are describing some existing techniques for detection of rusting. We have analyzed these
techniques and made comparison based o their approaches,
strengths and limitations.

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.675.7088&rep=rep1&type=pdf

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Highly recommend MIT 6.S191: Introduction to Deep Learning Course by Alexander Amini and Ava Soleimany through Massachusetts Institute of Technology (MIT)

#artificialintelligence
#deeplearning
#machinelearning
#CNN
#vision

MIT Deep Learning Course 6.S191 https://www.linkedin.com/pulse/mit-deep-learning-course-6s191-bhagirath-kumar-lader

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