FML: Face Model Learning from Videos Multi-frame video-based self-supervised learning to reconstruct 3D faces, without strong face priors.

https://gvv.mpi-inf.mpg.de/projects/FML19/

🗣 @AI_Python_Arxiv
✴️ @AI_Python_EN

The power of correctly framing your data science results...

Let's say you create a model that predicts sales leads with 3% accuracy, while only 1% of the population are true leads.

1. Your model is wrong 97% percent of the time. (Ouch.)
2. Your model is right 300% as often as a baseline approach. (Nice.)

Which one sounds better?

They're both saying the exact same thing, but presented in a different way. They have been framed differently.

And people will react to them very differently.

👉 What does this mean for data scientists?

It means that when you have a good result, it is not enough to simply present the numbers. You must frame them appropriately for the audience to understand the value of the work.

When presenting, make sure that you understand the needs and expectations of your audience so that you can communicate in way that presents your results in a favorable light.

✅ Focus on the positive, not the negative.

✅ Focus on improvements, not shortcomings.

✅ Focus on opportunities, not problems.

✅ Focus on what you learned, not where you failed.

#datascience #cognitivebiases #communication

🗣 @AI_Python_Arxiv
✴️ @AI_Python_EN

Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Network

Paper by Alex Sherstinsky: https://lnkd.in/enTMCDH

#RecurrentNeuralNetwork #RNN #LongShortTermMemory #LSTM #NeuralNetworks

🗣 @AI_Python_Arxiv
✴️ @AI_Python_EN

Best Paper Awards in Computer Science (since 1996)

A well maintained list: https://lnkd.in/e6_ks3E

#artificialintelligence #machinelearning #papers #research

🗣 @AI_Python_Arxiv
✴️ @AI_Python_EN

How to get started with data science if you don't like learning theory:

The best thing to do in your situation is to find a project and start working on it.

➡️ Grab a dataset and formulate a problem that you think you can solve using the data.

Then, begin working through solving it.

As you get stuck, hop online or grab a book and learn what you need to keep pushing the project forward.

Then, once you finish the project, you can evaluate weaknesses and find areas that can be improved.

Again, go back and learn what you need to learn to improve your project.

You can repeat this iterative process as many times as you need to until you've got something that really makes you a standout candidate and you can start showing it off in your portfolio.

➡️ Here are a few places to find datasets to get you started:

Kaggle datasets - https://lnkd.in/gzz_ZWd
UCI dataset repo - https://lnkd.in/g_f8sag
Google dataset search - https://lnkd.in/egee4gR

#datascience #machinelearning

🗣 @AI_Python_Arxiv
✴️ @AI_Python_EN

A new study published in Nature shows we can predicting the genetic disorder directly from the face using deep learning. The network was trained on a dataset of 17,000 patient images representing more than 200 syndromes. The paper reports that model achieves 91% top-10-accuracy in identifying the correct syndrome on 502 images and outperformed expert clinicians in three experiments. The method can be used to diagnose dysmorphology syndromes which typically affect roughly 1 in 30,000 people.
While this work has a great potential to improve discovering rare diseases, insurance companies may use this technology to deny providing medical insurance or increase the policy fees for people with specific genes.
paper: https://lnkd.in/fUEpYRt
#artificialintelligence #syndrome #ai #deeplearning #research

🗣 @AI_Python_Arxiv
✴️ @AI_Python_EN

PhDs linked to #DataScience or #artificialintelligence

Turing Institute PhD Studentships

https://www.qmul.ac.uk/scholarships/items/turing-institute-phd-studentships.html

🗣 @AI_Python_Arxiv
✴️ @AI_Python_EN

Some publication statistics for 2018 in #MachineLearning and Natural Language Processing #NLP

https://t.co/e4JbOZyh2i

🗣 @AI_Python_Arxiv
✴️ @AI_Python_EN

a database for students looking for scholarships, bursaries, grants and student awards.
https://www.scholarshipscanada.com/

🗣 @AI_Python_Arxiv
✴️ @AI_Python_EN

Python for web (pypy.js)

https://pypyjs.org/

❇️ @AI_Python
🗣 @AI_Python_Arxiv
✴️ @AI_Python_EN

Learning concept is one thing but to know how to apply them is another. While learning theoretical concepts most of us lack practical knowledge since it's hard to apply them simultaneously and write codes.

But Thanks to Michael Kroeker and Deep Learning Studio by Deep Cognition which always helped me to solve many problem easily and in less than no time

Now I can learn concepts and apply them simultaneously by a newly launched course on Udemy that will help you build neural networks in seconds.

Check it out here:
https://lnkd.in/eVbm576

Here you'll learn

-How To Build Deep Neural Networks In Seconds Using Deep Learning Studio.
-Rapidly Build And Visualize Neural Networks Without Programming Skills.
-How To Understand Neural Networks Without Math Formulas.
-How To Build Neural Networks Without Programming.
-How To Deploy Machine Learning Models Built Using Deep Learning Studio.
-Understand Normalization,Dropout Without Heavy Math Or Complicated Technical Explanations.
and more...

#machinelearning #deeplearning #programming #learning

🗣 @AI_Python_Arxiv
✴️ @AI_Python_EN

Analysis Methods in Neural Language Processing: A Survey

Paper by Yonatan Belinkov, James Glass: https://lnkd.in/e9WDDpZ

#naturallanguageprocessing #deeplearning #ai #artificialintelligence #machinelearning

🗣 @AI_Python_Arxiv
✴️ @AI_Python_EN

NeurIPS 2018

Videos: https://lnkd.in/edah9MA

#artificialintelligence #deeplearning #machinelearning #NeurIPS #NeurIPS2018

🗣 @AI_Python_Arxiv
✴️ @AI_Python_EN

#NLP is among the hottest and most interesting fields in #datascience. Check out these 5 in-depth and hands-on tutorials to learn NLP:

• The Essential NLP Guide to Solve Top 10 Common NLP Tasks - https://bit.ly/2QCCgR1
• Practical Tutorial for Regular Expressions in #Python - https://bit.ly/2QBChVi
• A Gentle Introduction to #TopicModeling - https://bit.ly/2QCCh7x
• Comprehensive and Intuitive Guide to #WordEmbeddings - https://bit.ly/2VKR4Av
• #TextClassification using ULMFiT and fastai Library in Python - https://bit.ly/2VHHEGa

And test your #NaturalLanguageProcessing knowledge on this challenging question set!

• 30 Questions to test a data scientist on Natural Language Processing - https://bit.ly/2jfGGyT

🗣 @AI_Python_Arxiv
✴️ @AI_Python_EN

There’s hundreds model-type on machine learning, there’s the most often algorithm used, because sometimes accuracy/simplicity #MachineLearning:

- - -
1. Logistic Regression
https://lnkd.in/gJ2BwhD

2. Decision Trees
https://lnkd.in/gwadA-p

3. Random Forests
https://lnkd.in/gRYHcvt

4-5. Neural Networks (RNN and CNN)
https://lnkd.in/gZQhWyv

6. Bayesian Techniques
https://lnkd.in/gY3qVYP

7. Support Vector Machines
https://lnkd.in/gWJKRyn

8. XGBoost
https://lnkd.in/gv85yDV

9. Light GBM
https://lnkd.in/gTBUtN4

10. Catboost
https://lnkd.in/gFPzuTx

11 Greedy Boost
https://lnkd.in/ghG-giR

12. Elastic Net
https://lnkd.in/g-NMjPb

13. Vowpal Wabbit
https://lnkd.in/g2W9qbD

It goes into great detail and explains the concepts in a simple way!

#artificialintelligence #datascience #python #statistics

🗣 @AI_Python_Arxiv
✴️ @AI_Python_EN

Exploring Quantum Neural Networks

#NeuralNetworks #Quantum

https://bit.ly/2VLVqaP

🗣 @AI_Python_Arxiv
✴️ @AI_Python_EN

Elon Musk Releases a Photo of His Latest Rocket, And It's Straight of Science Fiction

🗣 @AI_Python_Arxiv
✴️ @AI_Python_EN

The videos of our NeurIPSConf workshop on security in machine learning are now up. You can now watch all of the contributed and invited talks if you were not able to attend in person! Playlist with all of the talks is here:
https://www.youtube.com/playlist?list=PLFG9vaKTeJq4IpOje38YWA9UQu_COeNve

🗣 @AI_Python_Arxiv
✴️ @AI_Python_EN

Hyper-parameters of Machine Learning algorithms

#machinelearning #datascience #deeplearning #statistics #algorithms

🗣 @AI_Python_Arxiv
✴️ @AI_Python_EN

View Computer Musings, lectures given by Donald E. Knuth, Professor Emeritus of the Art of Computer Programming at Stanford University. The Stanford Center for Professional Development has digitized more than one hundred tapes of Knuth's musings, lectures, and selected classes and posted them online. These archived tapes resonate with not only his thoughts, but with insights from students, audience members, and other luminaries in mathematics and computer science. They are available to the public free of charge.

https://www.youtube.com/playlist?list=PL94E35692EB9D36F3

🗣 @AI_Python_Arxiv
✴️ @AI_Python_EN