I have an opening for a 4y PhD position in my ERC Consolidator project DREAM (“distributed dynamic representations for dialogue management”) at #ILLC in Amsterdam. Deadline 25 Feb 2019. More details: http://www.illc.uva.nl/NewsandEvents/News/Positions/newsitem/10538/
Please spread the word! #NLProc AmsterdamNLP

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I have funding for a Ph.D student to work in the general area of multimodal machine learning from images, videos, audio, and multilingual text. Please get in touch if you are interested.

elliottd.github.io

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The Language in Interaction research consortium invites applications for a postdoctoral position in Linguistics! We are looking for a candidate with a background in theoretical and/or computational linguistics. More information can be found here: https://www.mpi.nl/people/vacancies/postdoc-position-in-linguistics-for-research-consortium-language-in-interaction

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PhD position available
https://lsri.info/2019/02/01/phd-position-available/

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PhD position in nice lab on nice topics.

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A PhD position is available in my group to study the structure, regulation, and functioning of intercellular nanotubes in bacteria starting asap. This is a project I am very excited about. Please RT or contact me when you are interested. https://www.uni-osnabrueck.de/universitaet/stellenangebote/stellenangebote_detail/1_fb_5_sfb_research_assistant.html

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DASK CHEATSHEET - FOR PARALLEL COMPUTING IN DATA SCIENCE

You will need Dask when the data is too big

This is the guide from Analytics Vidhya https://lnkd.in/fKVBFhE

#datascience #pydata #pandas
#datascientist

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💡 What is the curse of dimensionality?

The curse of dimensionality refers to problems that occur when we try to use statistical methods in high-dimensional space.

As the number of features (dimensionality) increases, the data becomes relatively more sparse and often exponentially more samples are needed to make statistically significant predictions.

Imagine going from a 10x10 grid to a 10x10x10 grid... if we want one sample in each "1x1 square", then the addition of the third parameter requires us to have 10 times as many samples (1000) as we needed when we had 2 parameters (100).

In short, some models become much less accurate in high-dimensional space and may behave erratically. Examples include: linear models with no feature selection or regularization, kNN, Bayesian models

Models that are less affected by the curse of dimensionality: regularized models, random forest, some neural networks, stochastic models (e.g. monte carlo simulations)

#datascience #dsdj #QandA
#machinelearning

For more free info, sign up here -> https://lnkd.in/g7AYg72

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Stanford University - ML Group has released a python package called StanfordNLP build on PyTorch.

The best feature of this package is that it comes with pre-trained neural models for 53 human languages! Presumably the most number of pretrained models in any popular NLP package.

You can find more details here:
https://lnkd.in/f5yaJFK

#datascience #nlp #machinelearning

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Deep Unsupervised Learning Course Spring 2019
Berkeley University

Instructors: Pieter Abbeel, Peter Chen, Jonathan Ho, Aravind Srinivas
https://sites.google.com/view/berkeley-cs294-158-sp19/

Course : Machine Learning for Health
Toronto University: Spring 2019
Instructor: Dr. Marzyeh Ghassemi

https://cs2541-ml4h2019.github.io/

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The only notebook on the planet that (i know of) shows you how to Install TensorRT on Google Collab and then run an optimized VGG graph:

https://lnkd.in/e_rP5dU

https://lnkd.in/estbghA

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AAAI Conference Analytics

Citation distribution by the top AAAI 20 authors, year by year: https://lnkd.in/eV3YA5h

#artificalintelligence #deeplearning
#machinelearning

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Machine Learning is Much More Than Just Deep Learning

Deep learning is the most well-known of machine learning techniques, but far from the only one. If you don’t have a lot of data, techniques like linear regression work well. If there is more data, or the data is likely to have non-linearity, I’d recommend decision trees or decision forests.

Deep learning works very well with massive sets of images or similar data. But come with serious challenges to cost, time, and complexity.. Labeling the massive set of data required can be time consuming or expensive - especially if you need to pay others to label your data. Deep learning also requires considerable time to train, both training on a large data set as well as for the considerable parameter optimization.

A recent paper used Deep Learning to predict age from blood. The authors included a comparison of their Deep Learning algorithm to other machine learning techniques and discovered that a simpler technique generated similar accuracy. But, I’ve seen other papers where Deep Learning is the only technique used. When I see this I think of the saying, “When all you have is a hammer, the world looks like a nail.”

What do you think? Did I miss your favorite machine learning technique? #machinelearning #ai #datascience

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Nice overview of unsupervised pre-trained language models
https://lilianweng.github.io/lil-log/2019/01/31/generalized-language-models.html

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Generalized Language Models

Blog by Lilian Weng: https://lnkd.in/eJPgKWm
Share us With Your Friend!
#artificalintelligence #NLP #unsupervisedlearning

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Fixup Initialization: Residual Learning Without Normalization

Paper by Zhang et al.: https://lnkd.in/e6egt6x

PyTorch code by Andy Brock: https://lnkd.in/evhuhdj

#artificalintelligence #deeplearning #machinelearning

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Want to become an SQL expert for Data analysis and Manipulation? If Yes, then continue reading....

Last month I shared T-SQL Interview Ques. & Ans. to help job seekers in cracking the interview. My post went viral on linkedin and I helped thousand of users across the globe.

Today I'm sharing the best resources on internet from where you can gain advance Sql knowledge and practice real life SQL scenarios.

1. Books to Read: T-SQL Fundamentals by Itzik Ben-Gan ; SQL Server T-SQL Recipes by David Dye to practice SQL excercises.

2. Community to Join: SQLSERVERCENTRAL.COM | Best SQL community to gain advance level knowledge. Stairway series on this community is very useful to gain sound knowledge with Step-by-Step approach. There are also lot of good articles to read.

3. People to follow : My favorite Jeff Moden . He is MVP and helped thousands of people with his knowledge on above community. Brent Ozar is also MVP and very good SQL Mentor.

4. Site to Register: http://www.sql-ex.ru/ - My personal favorite to practice SQL & compete with Masters. The best thing about this site is you won't be able to reach next level until you solve the current level challenge.

Please share post so that it will help maximum people.

Learn....Earn....and Return !

#sql

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I've had a strong interest in psychometrics for many years. Some basic understanding of it is essential for marketing researchers as well as researchers and scholars in a diverse range of disciplines.

It's much more than Cronbach's alpha and principal components factor analysis with varimax rotation. Here are some books I can recommend about or related to psychometrics:

- Psychometrics (Furr and Bacharach)
- Measurement Theory and Applications for the Social Sciences (Bandalos)
- Bayesian Psychometric Modeling (Levy and Mislevy)
- Handbook of Item Response Theory Modeling (Reise and Revicki)
- The Theory and Practice of Item Response Theory (de Ayala)
- Multidimensional IRT (Reckase)
- Test Equating, Scaling, and Linking (Kolen and Brennan)
- Generalizability Theory (Brennan)
- Handbook of Personality Assessment (Weiner and Greene)
- Measures of Personality and Social Psychological Constructs (Boyle et al.)
- Cognitive Psychology (Sternberg and Sternberg)
- The Cognitive Neurosciences (Gazzaniga et al.)

There are many academic journals, such as the venerable Psychometrica, and three I currently subscribe to: Structural Equation Modeling (Routledge); Journal of Educational and Behavioral Statistics (ASA); and British Journal of Mathematical and Statistical Psychology (Wiley).

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we learned about strict separating hyperplane and Farkas' theorem last week in optimization class

🌎 Link

Special Thanks to :Mona Jalal

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WHAT IS A TENSOR?

Michel Van Biezen give a simple video explanation of what is a scalar, vector, dyad, Triad. They are all essentially Rank 0, 1, 2 and 3 Tensors.

Einstein's Field Equation (3 for x,y,z axes for space and another one for time) is essentially is a Rank 4 Tensor.

In matrices representation you can see how a Rank 3 Tensor is drawn.

Can you draw the Einstein's Rank 4 (256 components) tensor now?

Detail here https://lnkd.in/g9yAGS2

#deeplearning #fundamentals #artificialintelligence #tensor #matrices

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I think this can be great place to geek out about:

- Distributed processing
- Big Data and Platform Architecture
- SQL and NoSql databases
- Visualization tools

To help you learn interesting stuff and get your ideas and knowledge on front of people. Build your reputation to easier find a job if you need one.

So, please check it out, become a writer and send in your articles 📕

Plumbers of Data Science on Medium:
https://lnkd.in/dU4fPRU

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