Checkout these new free resources in #DataScience👇

1. Introduction to PyTorch for Deep Learning: https://lnkd.in/f7kqZS2

2. Pandas for Data Analysis in Python: https://lnkd.in/fvRQHww

3. Support Vector Machine (SVM) in Python and R: https://lnkd.in/faJcSHe

4. Fundamentals of Regression Analysis: https://lnkd.in/fnEDP78

5. Getting started with Decision Trees: https://bit.ly/2PuZRFB

6. Introduction to Neural Networks: https://lnkd.in/fYUnsYQ

this is great work that collects corpora and evaluates models for two extremely low-resource languages spoken in Africa
Earth globe europe-africa
, Twi and Yoruba.
Link to the paper: https://arxiv.org/abs/1912.02481

❇️ @AI_Python_EN

If you want to learn about privacy-preserving machine learning, then there is no better resource than this step-by-step notebook tutorial by Andrew Trask
.

From the basics of private deep learning to building secure ML classifiers using PyTorch & PySyft.
https://github.com/OpenMined/PySyft/tree/master/examples/tutorials

❇️ @AI_Python_EN

Factor analysis, in which both latent (unobserved) and manifest (observed) variables are continuous, is perhaps the best known.

In latent profile analysis the latent variable (e.g. consumer segments) is categorical and the manifest variables (e.g. responses to rating scales) are continuous.

Latent trait models (e.g. item response theory) are characterized by continuous latent variables and categorical manifest variables (e.g. correct or incorrect answers to test items).

In latent class analysis both latent and observed variables are categorical.

There are also hybrid models which include both continuous and categorical latent and manifest variables.

In some models there is a distinction between dependent and independent variables. Censored, truncated and count variables can also be accommodated.

Any of these models can be multilevel (hierarchical) or longitudinal and can incorporate exogenous variables (covariates).

This popular book is focused on latent class analysis and its longitudinal extension, latent transition analysis. It is well written and covers theoretical and technical issues as well as application.

https://www.google.com/search?kgmid=/g/12bmhby6b&hl=en-JP&kgs=a09137cca2d41ecf&q=Latent+Class+and+Latent+Transition+Analysis:+With+Applications+in+the+Social,+Behavioral,+and+Health+Sciences&shndl=0&source=sh/x/kp/osrp&entrypoint=sh/x/kp/osrp

❇️ @AI_Python_EN

[MobiNetV1] Removing people from complex backgrounds in real time using TensorFlow.js in the web browser!

This code attempts to learn over time the makeup of the background of a video such that the algorithm can attempt to remove any humans from the scene. This is all happening in real time, in the browser, using TensorFlow.js.


https://lnkd.in/gsePqBH

#deeplearning #machinelearning #artificialintelligence

❇️ @AI_Python_EN

PyTorch Implementation and Explanation of Graph Representation Learning papers involving DeepWalk, GCN, GraphSAGE, ChebNet & GAT.


https://github.com/dsgiitr/graph_nets

❇️ @AI_Python_EN

jeremy howardWe're launching fastpages, a platform which allows you to host a blog for free, with no ads. You can blog with ProjectJupyter
notebooks, office
Word, directly from github
's markdown editor, etc.

Nothing to install, & setup is automated!

https://fastpages.fast.ai/fastpages/jupyter/2020/02/21/introducing-fastpages.html

❇️ @AI_Python_EN

Localized Narratives multi-modal annotations released!
White heavy check mark 628k images, White heavy check mark 6400 km of mouse traces,White heavy check mark 1.5 years of voice recordings,White heavy check mark
650k captions.All synchronized.
https://google.github.io/localized-narratives/

❇️ @AI_Python_EN

When ML models are deployed, data distributions evolving over time leads to a drop in performance. Our latest paper (theory and experiments) suggests we can use self-training on unlabeled data to maintain high performance
https://arxiv.org/pdf/2002.11361.pdf

❇️ @AI_Python_EN

You can use a method called one hot encoding.
https://hackernoon.com/what-is-one-hot-encoding-why-and-when-do-you-have-to-use-it-e3c6186d008f

Covid-19, your community, and you — a data science perspective

https://www.fast.ai/2020/03/09/coronavirus/

❇️ @AI_Python_EN

How neural structure learning can help avoid adversarial attack om models

https://medium.com/@omcar17/what-is-neural-structure-learning-4d4e43083df9

❇️ @AI_Python_EN

http://bit.ly/38PUGGO here's a list of most popular ML interview questions and answers.

Here's an update from Dan Jurafsky and the #acl2020nlp team re COVID19:

https://acl2020.org

#NLProc

Can a shiny app be a paper? Heck yeah!
Red question mark ornament
"Where to publish your Shiny App?"

https://buff.ly/3cOqSNU #rstats #rshiny

We have just released Multi-SimLex v1: a new multilingual #NLProc resource for semantic similarity. It covers 1,888 concept pairs across 12 typologically diverse langs, plus 66 xling data sets. .

https://multisimlex.com

Multi-SimLex provides a new, typologically diverse evaluation benchmark for representation learning models. See our paper for experiments and interesting analysis:

https://arxiv.org/pdf/2003.04866.pdf

But this is not all! We are also launching a collaborative initiative to extend Multi-SimLex to cover many more of the world’s languages! Please join us in this effort to create an extensive semantic similarity resource for the needs of contemporary multilingual #NLProc.We welcome your contributions for both small and major languages! Follow the guidelines at https://multisimlex.com to create and submit a Multi-Simlex -style dataset for your favourite language. All the
contributions will be shared with everyone via the Multi-SimLex site.

Abnormal respiratory patterns classifier may contribute to large-scale screening of people infected with COVID-19 in an accurate and unobtrusive manner.

abs: https://arxiv.org/abs/2002.05534v1

#rnn #machinelearning #ArtificialIntelligence #DeepLearning #

❇️ @AI_Python_EN

Introduction to Reinforcement Learning

By DeepMind : https://lnkd.in/dd2VNhH

#ArtificialIntelligence #DeepLearning #ReinforcementLearning