Google's Open Images V4 is a publicly available dataset that contains 15.4M annotated bounding boxes for over 600 object categories. It has 1.9M images and is largest among all existing public image datasets with object location annotations.

We share a tutorial with a script for a fast downloader that allows you to filter and download images based on various classes and categories of your interest.

https://lnkd.in/gspXR6K

If you find this tool useful, please share.

#computervision #machinelearning #ai #training #objectdetection #deeplearning

✴️ @AI_Python_EN
❇️ @AI_Python
🗣 @AI_Python_arXiv

A useful guide for all beginners in #machinelearning & #datascience - It lists down the most active data scientist on github, free books, ipython notebooks, tutorials on github. https://bit.ly/2I9xmvM

✴️ @AI_Python_EN
❇️ @AI_Python
🗣 @AI_Python_arXiv

Getting that first break in #DataScience can be quite tough. Here are 3 inspiring stories from our community on how they transitioned into data science from other fields:

Step by Step process of How I Became a #MachineLearning Expert in 10 Months - https://lnkd.in/fUwbkR5 Madhukar Jha

Journey from an IT Engineer to Head of #Analytics - https://lnkd.in/fFPDC-Z Ritesh Mohan Srivastava

From a Paper Delivery Boy to a Lead #DataEngineer & #QlikView Luminary - https://lnkd.in/fKpgKyf

✴️ @AI_Python_EN
❇️ @AI_Python
🗣 @AI_Python_arXiv

WHAT IS USE CASES DATA SCIENCE IN HR?

Data science is not only intended for those who want to become data scientists. Data Science is a science that can be applied to HR as well. Here are some reasons why a recruiter also needs to learn data science:

1. Strategic recruitment by learning data science
https://lnkd.in/f3S8zvA
2. Future recruitment processes are in AI
https://lnkd.in/fqvemej
3. Data Science selection process
https://lnkd.in/fiCRwPW
4. You can become a Recruitment Specialist in data science.
https://lnkd.in/fNGvtpG
5. You can design a dashboard that is ideal for the recruitment process.
https://lnkd.in/fghidHC
6. Insight on learning data science
https://lnkd.in/g5n3bRn
7. Predicting Employe Turover
https://lnkd.in/fkMu3A6
8. AI for Candidate Selection
https://lnkd.in/f7Kf3Mf
9. Increasing Employee Happiness
https://lnkd.in/fTzNbsC
10. Predicting Performance
https://lnkd.in/fdCmR-B

#datascience #humanresource #artificialinteligence #analytics

✴️ @AI_Python_EN
❇️ @AI_Python
🗣 @AI_Python_arXiv

BERT: Pre-training of #deeplearning Bidirectional Transformers for Language Understanding

BERT slides

✴️ @AI_Python_EN
❇️ @AI_Python
🗣 @AI_Python_arXiv

✴️ @AI_Python_EN
❇️ @AI_Python
🗣 @AI_Python_arXiv

Generalization through Simulation: Integrating Simulated and Real Data into Deep Reinforcement Learning for Vision-Based Autonomous Flight https://arxiv.org/abs/1902.03701

✴️ @AI_Python_EN
❇️ @AI_Python
🗣 @AI_Python_arXiv

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

✴️ @AI_Python_EN
❇️ @AI_Python
🗣 @AI_Python_arXiv

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

✴️ @AI_Python_EN
❇️ @AI_Python
🗣 @AI_Python_arXiv

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/

✴️ @AI_Python_EN
❇️ @AI_Python
🗣 @AI_Python_arXiv\

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

✴️ @AI_Python_EN
❇️ @AI_Python
🗣 @AI_Python_arXiv\

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

✴️ @AI_Python_EN
❇️ @AI_Python
🗣 @AI_Python_arXiv

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

✴️ @AI_Python_EN
❇️ @AI_Python
🗣 @AI_Python_arXiv

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

✴️ @AI_Python_EN
❇️ @AI_Python
🗣 @AI_Python_arXiv

#DataScience #statistics #MachineLearning
Happy (nerdy) Valentines Day
✴️ @AI_Python_EN
❇️ @AI_Python
🗣 @AI_Python_arXiv

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/

✴️ @AI_Python_EN
❇️ @AI_Python
🗣 @AI_Python_arXiv

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

✴️ @AI_Python_EN
❇️ @AI_Python
🗣 @AI_Python_arXiv

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

✴️ @AI_Python_EN
❇️ @AI_Python
🗣 @AI_Python_arXiv

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

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

✴️ @AI_Python_EN
❇️ @AI_Python
🗣 @AI_Python_arXiv

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

https://github.com/Microsoft/Recommenders

✴️ @AI_Python_EN
❇️ @AI_Python
🗣 @AI_Python_arXiv

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

✴️ @AI_Python_EN
❇️ @AI_Python
🗣 @AI_Python_arXiv