AI, Python, Cognitive Neuroscience

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

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


https://github.com/dsgiitr/graph_nets

❇️ @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

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

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

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

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

"The next revolution of AI won’t be supervised." — Yann LeCun

#ArtificialIntelligence #SemiSupervisedLearning #UnsupervisedLearning

❇️ @AI_Python_EN

Deep Learning Has Limits. But Its Commercial Impact Has Just Begun.

Rob Toews, Forbes: https://lnkd.in/e6MS8tu

#ArtificialIntelligence #Business #DeepLearning

I’m the Inclusion Content Manager for Amazon Web Services (AWS) and I’ll looking for Canadian speakers in machine learning for our Ottawa Summit.

If you fit this criteria and are working for companies that are building on AWS, please reach out to me at diykhann@amazon.com.

Diya Khanna • diya.khanna@gmail.com

❇️ @AI_Python_EN

Description

The Department of Computer Science at Smith College invites applications for two, two-year, benefits-eligible positions at the rank of Visiting Assistant Professor of Computer Science, to begin July 1, 2020.  The successful candidates will be prepared to teach five courses per year and must provide evidence of excellence in teaching and in research. The positions are open-field. The term of appointment for each position is for two years, ending June 2022. A Ph.D. in computer science or a closely related field is expected by the time of appointment. Candidates with the ability to work with diverse student populations are especially encouraged to apply.


Located in Northampton, MA, Smith College is the largest women’s college in the country and is dedicated to excellence in teaching and research across the liberal arts.  The faculty of outstanding scholars interact with students in small classes, as advisors, and through student-faculty research projects. Smith College offers opportunities to foster faculty success at every career stage, such as those listed here: https://www.smith.edu/about-smith/provost/faculty-development.  The College is a member of the Five College Consortium with Amherst, Hampshire and Mt. Holyoke Colleges, and the University of Massachusetts Amherst.  Students cross-enroll and faculty cross-teach across the Five Colleges. Details about the Department of Computer Science may be found at http://cs.smith.edu/ .


Diversity and a culture of equity and inclusion among students, staff, faculty, and administration are crucial to the mission and values of Smith College. We are an Affirmative Action/Equal Opportunity employer and do not discriminate on the basis of race, gender, age, color, religion, national origin, disability, sexual orientation, gender identity and expression or veteran status in the recruitment and employment of faculty and staff, and the operation of any of its programs and activities, as specified by all applicable laws and regulations. Women, historically underrepresented minorities, veterans, and individuals with disabilities are encouraged to apply.


Application Instructions

Submit application at http://apply.interfolio.com/73031 with a cover letter, curriculum vitae, a teaching statement, a research statement, and three confidential letters of recommendation. Review of applications will begin on March 1, 2020. 

❇️ @AI_Python_EN

@ai_python_arXiv :
ArXiv Papers Related to Computer Science, AI, Deep Learning, Computer Vision, NLP, etc
From: @ai_python_en

Microsoft ImageBERT | Cross-modal Pretraining with Large-scale Image-Text Data

https://syncedreview.com/2020/01/27/microsoft-imagebert-cross-modal-pretraining-with-large-scale-image-text-data/amp/

$1 million AAAI Squirrel AI Award for Artificial Intelligence for the Benefit of Humanity!

https://lnkd.in/equKpQq

#HumanitarianAI

Forecasting at Uber: An Introduction
https://eng.uber.com/forecasting-introduction/

🔥 As you know ML has proven its importance in many fields, like computer vision, NLP, reinforcement learning, adversarial learning, etc .. Unfortunately, there is a little work to make machine learning accessible for Arabic-speaking people. Arabic language has many complicated features compared to other languages. First, Arabic language is written right to left. Second, it contains many letters that cannot be pronounced by most foreigners like ض ، غ ، ح ، خ، ظ. Moreover, Arabic language contains special characters called Diacritics which are special characters that help readers pronounced words correctly. For instance the statement السَّلامُ عَلَيْكُمْ وَرَحْمَةُ اللَّهِ وَبَرَكَاتُهُ containts special characters after most of the letters. The diactrics follow special rules to be given to a certain character. These rules are construct a complete area called النَّحْوُ الْعَرَبِيُّ. Compared to English, the Arabic language words letters are mostly connected اللغة as making them disconnected is difficult to read ا ل ل غ ة. ArbML helps fixing this by implementing many open-source projects that support Arabic, ML and NLP.

https://github.com/zaidalyafeai/ARBML

#machinelearning #deeplearning #artificialintelligence #nlp

❇️ @AI_Python_EN

https://medium.com/sfu-big-data/why-so-many-different-clustering-algorithms-2fd94906c668
Why so many different clustering algorithms?
Cluster analysis is an unsupervised learning task that aims to divide objects into groups based on their similarity.

We are looking for full time Data Analyst working with our industry partners, as well as contributing to our university's world class science and research.

https://jobs.illinois.edu/academic-job-board/job-details?jobID=125801&job=data-analyst-national-center-for-supercomputing-applications-125801

❇️ @AI_Python_EN