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

A PyTorch re-implementation of Generative Teaching Networks has been made available by GoodAIdev https://lnkd.in/giJBSw3 Nice to see! https://lnkd.in/gzGMJBn

Access 2 new free online courses
as of today on edXOnline
It's time to hone your #digitalintelligence knowledge and skills, even more if you're getting bored at home:
http://bit.ly/2WLF58R

#DeepLearning

❇️ @AI_Python_EN

Help us scale #COVID19 detection over the phone.

If you have a #COVID19 diagnosis or are healthy, consider recording a breathing sample anonymously at https://breatheforscience.com

We hope this data leads to techniques to help diagnosis of #COVID19 over the phone.

❇️ @AI_Python_EN

10 Useful ML Practices For Python Developers

Pratik Bhavsar:
https://medium.com/modern-nlp/10-great-ml-practices-for-python-developers-b089eefc18fc


#Python #MachineLearning #ArtificialIntelligence #DataScience #Programming

❇️ @AI_Python_EN

Contributor Derrick Mwiti with an overview of #TensorFlow MLIR—a mult-level intermediate representation designed to be a reusable and extensible compiler that works across the #DeepLearning landscape.
https://bit.ly/2Jt83T7

❇️ @AI_Python_EN

Excited to share that my first first-author paper - “Unsupervised Cross-lingual Representation Learning at Scale”

Link: https://arxiv.org/pdf/1911.02116.pdf

❇️ @AI_Python_EN

NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis

Input is images with known camera poses. Can't wait to disable all the sanity checks and see what this renders if I give it impossible geometry.

abs: https://arxiv.org/abs/2003.08934
site: http://matthewtancik.com/nerf

❇️ @AI_Python_EN

Breast cancer classification with Keras and Deep Learning

To analyze the cellular structures in the breast histology images we were instead leveraging basic computer vision and image processing algorithms, but combining them in a novel way.

Researcher: Adrian Rosebrock
Paper & codes : http://ow.ly/yngq30qjLye

#artificialintelligence #ai #machinelearning #deeplearning #bigdata #datascience

❇️ @AI_Python_EN

Very good news. Dataproc now lets you use NVIDIA GPUs to accelerate XGBoost in a Spark pipeline. This combination can speed up machine learning development and training up to 44x and reduce costs 14x when using XGBoost. With this kind of GPU acceleration for XGBoost, you can get better performance, speed, accuracy, and reduced TCO, plus an improved experience when deploying and training models. Spinning up elastic Spark and XGBoost clusters in Dataproc takes about 90 seconds.
https://gweb-cloudblog-publish.appspot.com/products/data-analytics/ml-with-xgboost-gets-faster-with-dataproc-on-gpus/amp/

#spark #machinelearning #xgboost #nvidia #gpu

❇️ @AI_Python_EN

article on handling railway disruptions with uncertain durations by a novel rolling-horizon two-stage stochastic method.

https://www.sciencedirect.com/science/article/pii/S2210970619300794?via%3Dihub

❇️ @AI_Python_EN

Increase the topic coherence of your neural variational topic models using pre-trained representations from BERT!

Paper: https://arxiv.org/abs/2004.03974

https://github.com/MilaNLProc/contecontextualizedxtualized-topic-models

❇️ @AI_Python_EN

ANNOUNCING PYCARET 1.0.0 - An amazingly simple, fast and efficient way to do machine learning in Python. NEW OPEN SOURCE ML LIBRARY If you are a DATA SCIENTIST or want to become one, then this is for YOU....

PyCaret is a NEW open source machine learning library to train and deploy ML models in low-code environment.

It allows you to go from preparing data to deploying a model within SECONDS.

PyCaret is designed to reduce time and efforts spent in coding ML experiments. It automates the following:

- Preprocessing (Data Preparation, Feature Engineering and Feature Selection)
- Model Selection (over 60 ready-to-use algorithms)
- Model Evaluation (50+ analysis plots)
- Model Deployment
- ML Integration and Monitoring (Power BI, Tableau, Alteryx, KNIME and more)
- ..... and much more!

Watch this 1 minute video to see how PyCaret can help you in your next machine learning project.

The easiest way to install pycaret is using pip. Just type "pip install pycaret" into your notebook.

To learn more about PyCaret, please visit the official website https://www.pycaret.org

#datascience #datascientist #machinelearning #ml #ai #artificialintelligence #analytics #pycaret

❇️ @AI_Python_EN

🎯 Deep Learning Based Text Classification: A Comprehensive Review

🗣 Shervin Minaee, Nal Kalchbrenner, Erik Cambria, Narjes Nikzad, Meysam Chenaghlu, Jianfeng Gao

https://arxiv.org/abs/2004.03705

❇️ @AI_Python_EN

SOTA results for Visual Exploration, CVPR-19 Habitat Challenge Winner, Sim-to-real, code, pretrained models and more!
Learning to Explore using Active Neural SLAM

Webpage:
https://devendrachaplot.github.io/projects/Neural-SLAM

Code:
https://github.com/devendrachaplot/Neural-SLAM


PDF:
https://arxiv.org/abs/2004.05155

❇️ @AI_Python_EN

Hello Learners,
Under affordable AI initiative, we are starting a new batch for Affordable Deep Learning And Advanced NLP batch. The time duration of the course will be for

link for registration
http://ineuron1.viewpage.co/Deep-learning-masters-with-NLP-and-computer-vision-krish-naik

5 months and 3 months of remote internship.
The course will be starting on April 18th, 2020.
he Live Online sessions will be held on Saturday and Sunday from 8 PM IST to 10 PM IST and Thursday 8 PM IST TO 10 PM IST for doubt clearing session

The course cost is 3000 INR + 18% GST for the whole course. All the sessions will be live online and it will be recorded. Please utilize this opportunity to upskill urself. Please check the below link for the syllabus and save your spot. The support team will call you for the registration. Happy Learning!!

Prerequisite is you need to know python. 60 hours of recorded videos will be available for python also.

❇️ @AI_Python_EN