Excited to announce StanfordNLP, a natural language processing toolkit for 53 languages with easily accessible pretrained models. It allows you to tokenize, tag, lemmatize, and (dependency) parse many languages, and provides a Python interface to CoreNLP.

https://stanfordnlp.github.io/stanfordnlp/

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You will never be a data scientist without knowing #Calculus, #Probability and #InformationTheory.

www.interviews.ai

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Shuffling large datasets, have you ever tried that?

Here the author presents an algorithm for shuffling large datasets.
Here you learn the following;

0. why Shuffle in the first place?
1. A 2-pass shuffle algorithm is tested
2. How to deal with oversized piles
3. Parallelization & more

Link to article : https://lnkd.in/dZ8-tyJ
Gist on #Github: for a cool visualization of the shuffle https://lnkd.in/d8iK8fd

#algorithms #github #datasets #deeplearning #machinelearning

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Foundations Built for a General Theory of Neural Networks

"Neural networks can be as unpredictable as they are powerful. Now mathematicians are beginning to reveal how a neural network’s form will influence its function."

Article by Kevin Hartnett: https://lnkd.in/eZa5eyX

#artificialneuralnetworks #artificalintelligence #deeplearning #neuralnetworks #mathematics

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FREE DATA SCIENCE RESOURCE

After long hours curated content on data science resource during 2018, http://www.claoudml.co/ list that actually we have lucrative resource to learn data science.

You can find it http://www.claoudml.co/
#technology #business #datascience

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Lots of data scientists work primarily with Jupyter notebooks. Have you considered writing your courses in Jupyter?

Jupyter notebooks and python modules (.py files) are both great tools, but they have different things that they do well. Notebooks are good for telling a story, walking through an analysis to answer a specific set of questions. Making a collection of .py files is good when creating a modular codebase, like when writing object-oriented code or building a more extensive set of tools. Modules can get unwieldy if all you're doing is a focused analysis with off-the-shelf tools, and notebooks get tricky to navigate if you are including very many functions. Notebooks excel at scripts, modules excel at classes and functions. They each have settings where they shine.

For the End-to-End Machine Learning scenarios we've walked through in our courses so far, the processing is complex enough that it doesn't lend itself well to a linear script. For that reason I've opted to go the module route. But I do so hesitantly because I know how many data scientists really like notebooks. I'll plan to use them in future courses that are a better fit for script-like code.

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2019 is the year of Artificial Intelligence.

This is the year that we replace one buzzword with another.

This will allow incompetent organizations that have failed to successfully leverage machine learning and data science in the past to make excuses and get another shot at the whole process...

Unfortunately, these companies that have failed to make the proper investment in data science (and failed to hire at the leadership level first) will just be wasting their money and getting burned again.

If you have the option, try to avoid these buzzword-laden hack shops.

If you're looking for a job, look for companies that have been investing in data science for years and building over time... not constantly rebranding their failing departments and products to look like they have something "fresh."

The companies and individuals that make the long-term investments are the ones that will win out in the end.

Invest in yourself and find a company that invests in data science.

#datascience

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The Evolved Transformer

Paper by So et al.: https://lnkd.in/eNZ6ije

#artificalintelligence #MachineLearning #NeuralComputing #EvolutionaryComputing #research

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Invertible Residual Networks

Paper by Behrmann: https://lnkd.in/dDnrmhr

#MachineLearning #ArtificialIntelligence #ComputerVision #PatternRecognition

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This is a super cool resource: Papers With Code now includes 950+ ML tasks, 500+ evaluation tables (including SOTA results) and 8500+ papers with code. Probably the largest collection of NLP tasks I've seen including 140+ tasks and 100 datasets. https://paperswithcode.com/sota

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YOLOv3 still has the best introduction for any paper I've read so far https://pjreddie.com/media/files/papers/YOLOv3.pdf

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I have a fully-funded 4Y PhD position in applied #NLP (in the #IR context) available at Delft University of Technology. Get in touch if you are interested!

https://chauff.github.io/

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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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