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

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Best Paper Awards in Computer Science (since 1996)

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

#artificialintelligence #machinelearning #papers #research

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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

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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

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PhDs linked to #DataScience or #artificialintelligence

Turing Institute PhD Studentships

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

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

https://t.co/e4JbOZyh2i

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

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Python for web (pypy.js)

https://pypyjs.org/

❇️ @AI_Python
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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

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Analysis Methods in Neural Language Processing: A Survey

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

#naturallanguageprocessing #deeplearning #ai #artificialintelligence #machinelearning

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NeurIPS 2018

Videos: https://lnkd.in/edah9MA

#artificialintelligence #deeplearning #machinelearning #NeurIPS #NeurIPS2018

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#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

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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

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Exploring Quantum Neural Networks

#NeuralNetworks #Quantum

https://bit.ly/2VLVqaP

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

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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

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Hyper-parameters of Machine Learning algorithms

#machinelearning #datascience #deeplearning #statistics #algorithms

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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

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First lecture on Deep Learning Basics is up on YouTube (see link). It's an introductory lecture overviewing the basics of deep learning.

https://www.youtube.com/watch?v=O5xeyoRL95U

Slides for this lecture:

https://www.dropbox.com/s/c0g3sc1shi63x3q/deep_learning_basics.pdf

Website: https://deeplearning.mit.edu/
GitHub repo with tutorials: https://github.com/lexfridman/mit-deep-learning

For those around MIT, the course is open to all. It runs every day in January at 3pm

https://towardsdatascience.com/the-abcs-of-machine-learning-experts-who-are-driving-the-world-in-ai-2995a8115bea

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*** Data Science: Data Scientist Bias Machine-Learning ***
~ There are many ways data scientists can influence machine-learning learning.
~ Here are the top human failings:
1. The square peg bias. This is where you just choose the wrong data set because it's what you have.
2. Sampling bias. You choose your data to represent the population under study. Sometimes, you draw incorrectly from the right population, or draw from the wrong population.
3. Bias-variance trade-off. You may cause bias by overcorrecting for variance. If your model is too sensitive to variance, small fluctuations could cause it to model random noise. Too much bias to correct this could miss complexity.
4. Measurement bias. This is when the instrument you use to collect the data has built-in bias, say, a scale that incorrectly overestimates weight.


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