All 1,294 papers at #CVPR2019
Index: http://openaccess.thecvf.com/content_CVPR_2019/html/
#ArtificialIntelligence #DeepLearning #MachineLearning

✴️ @AI_Python_EN

Best of arXiv.org for AI, #MachineLearning, and Deep Learning – April 2019 by insidebigdata

https://insidebigdata.com/2019/05/22/best-of-arxiv-org-for-ai-machine-learning-and-deep-learning-april-2019/

✴️ @AI_Python_EN

Yann LeCun et al. publishing evolutionary algorithm tools. Welcoming the era of deep neuroevolution indeed! (https://eng.uber.com/deep-neuroevolution) Great to see the traditional ML community adopt these tools in the cases when they are useful.

✴️ @AI_Python_EN

Hot Papers from Google Brain, DeepMind and Facebook AI

https://www.google.com/amp/s/syncedreview.com/2019/06/02/hot-papers-from-google-brain-deepmind-and-facebook-ai/amp/

✴️ @AI_Python_EN

Material used for Deep Learning related workshops for Machine Learning Tokyo
Implementation and Cheat Sheet: https://github.com/Machine-Learning-Tokyo/DL-workshop-series
#artificialintelligence #deeplearning #machinelearning

✴️ @AI_Python_EN

This is probably the best #PyTorch Deep Learning course I have encountered.
https://fleuret.org/dlc/

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Below are 44 frequently asked question (and answer) on Deep Learning key principles

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Here is a full list of Iranian Female Researchers presenting at ICML poster sessions and workshops! Don't forget to support them!

https://lnkd.in/gbq2hmu

✴️ @AI_Python_EN

In my view, data are guilty until proven innocent. By that I mean we cannot assume they are error free and honest in what they seem to be telling us. Justice in statistics can be harsh. :-)

Statisticians working with consumer survey data for the first time are often alarmed when they dig into the data. They can be especially critical of the questionnaire, which may have been designed by a person or persons with no formal background in survey research.

Many questions may make little sense to most people, or have different meanings to different people. Answer categories may overlap or be ambiguous in other ways.

People vary in response styles too and, furthermore, their attention may wander during sections of the questionnaire of little interest to them.

In the worst case, important marketing decisions may be made on the basis of how respondents interacted with the survey instrument.

Fortunately, besides professional questionnaire design, there are statistical means psychometricians have developed which can help reduce the noise in survey data.

This is a big topic and I can't do justice to it in a short LI post, other than to say statisticians familiar with survey research now have a multitude of tools to help them clean survey data and avoid being "conned." .Kevin Gray

✴️ @AI_Python_EN

Top Java, Deep Learning, DevOps And AWS Interview Questions You Must Know (2019)

It is the updated version for anyone who is going to have an interview soon or even challenge yourself to test your understandings of #Deeplearning.

✅100+ #Java Interview Questions You Must Prepare In 2019:
https://lnkd.in/gr2djip
✅Most Frequently Asked #AI Interview Questions
https://lnkd.in/g6Q89dn
✅Top #AWS Architect Interview Questions In 2019
https://lnkd.in/gecpceu
✅Top #DevOps Interview Questions You Must Prepare In 2019
https://lnkd.in/gTCFCyt

✴️ @AI_Python_EN

*******The Algorithms*******

Open Source Resource for Newbies to Learn Algorithms and Implement them in any Programming Language.

Github Link - https://lnkd.in/edw2vHj

#pythonprogramming #python #java #scala #c #cplusplus #csharp

✴️ @AI_Python_EN

💡💡 Commonly used Machine Learning Algorithms 💡💡

Here is the list of commonly used machine learning algorithms. The code is provided in both #R and #Python. These algorithms can be applied to almost any data problem:

✅Linear Regression
✅Logistic Regression
✅Decision Tree
✅SVM
✅Naive Bayes
✅kNN
✅K-Means
✅Random Forest
✅Dimensionality Reduction Algorithms
✅Gradient Boosting algorithms
✔️GBM
✔️XGBoost
✔️LightGBM
✔️CatBoost

Credit: Analytics Vidhya,Sunil Ray

Thanks for the share Steve Nouri.

#datascience #deeplearning #ai #artificialintelligence #machinelearning #data #r #python

✴️ @AI_Python_EN

DETECTING FAKE NEWS

Online disinformation, or fake news intended to deceive, has emerged as a major societal problem. Currently, fake news articles are written by humans, but recently-introduced AI technology based on Neural Networks might enable adversaries to generate fake news. Our goal is to reliably detect this “neural fake news” so that its harm can be minimized.

To study and detect neural fake news, we built a model named Grover. Our study presents a surprising result: the best way to detect neural fake news is to use a model that is also a generator.

Learn more and try Grover by clicking the link below.

https://t.me/ai_python_en/1416

Google open-sources soccer reinforcement learning simulator
Article:
https://lnkd.in/eKw5Zie
Code:
https://lnkd.in/eiT9Z5j

#MachineLearning #ArtificialIntelligence #DataScience

✴️ @AI_Python_EN

An easy to follow and inspirational Blog about #PyTorch internals.
https://lnkd.in/efSEwpP

✴️ @AI_Python_EN

How Twitter and #MachineLearning (KDE + LDA) help to predict Crime?

📘 predict Crime

✴️ @AI_Python_EN

Lex Fridman (DeepTweets: Generating Fake Tweets with Neural Networks Trained on Individual Twitter Accounts)

I fine-tuned GPT-2 neural net on people's tweets to create #AI versions of them. Surprisingly realistic and at times profound. Here's a real tweet about tunnels from Elon Musk rewritten by AI versions of Just Bieber, Kanye West, and Katy Perry.
Details: https://lnkd.in/eaWkeqg

✴️ @AI_Python_EN

We're now available via #linkedin :)

link :
https://www.linkedin.com/groups/13723396/


✴️ @AI_Python_EN

Simulation of fan emotions during a basketball game:
https://flowingdata.com/2019/06/07/simulation-of-fan-emotions-during-a-basketball-game/

✴️ @AI_Python_EN

Free website design workshop in Boston
https://www.facebook.com/events/2424424517786811/?ti=icl

✴️ @AI_Python_EN

If you've read job descriptions in data lately you are probably confused. Are you a data scientist, machine learning engineer, or research scientist? Instead of title matching, try asking yourself these questions:

1. Can you use statistics to answer questions about a situation that is new to you? Meaning, is your comfort with stats solid enough that you can bring it to bear appropriately depending on scenario?

2. Can you explain why a particular model performs well in a scenario, rather than just noting it does well? Meaning, do you understand the inner workings of models to tune and make sense of why they do what they do?

3. If someone mentions time and space complexity to you, does it make sense? In a big data world, thinking carefully about load of a particular algorithm is extremely important. This matters particularly for MLE and science positions.

4. Can you build something new? Maybe there isn't a perfect algorithm for what you want. Maybe the package in R doesn't exist. Can you make it happen if you need to?

5. Do you know what it means to put something into production? Do you have examples of how you've succeeded or failed with this?

These questions are not all encompassing, but they point to some of the key skillsets you'll need.

#datascience #analytics #data

✴️ @AI_Python_EN