jeremy howardWe're launching
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
fastpages
, a platform which allows you to host a blog for free, with no ads. You can blog with ProjectJupyternotebooks, 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
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
google.github.io
Localized Narratives
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
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
https://hackernoon.com/what-is-one-hot-encoding-why-and-when-do-you-have-to-use-it-e3c6186d008f
Hackernoon
What is One Hot Encoding? Why And When do you have to use it? | HackerNoon
So, you’re playing with ML models and you encounter this “One hot encoding” term all over the place. You see the <a href="http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html" target="_blank">sklearn documentation</a>…
Covid-19, your community, and you — a data science perspective
https://www.fast.ai/2020/03/09/coronavirus/
❇️ @AI_Python_EN
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
https://medium.com/@omcar17/what-is-neural-structure-learning-4d4e43083df9
❇️ @AI_Python_EN
Medium
What is Neural Structure Learning?
This blog is an introduction to Neural Structure Learning (NSL).
http://bit.ly/38PUGGO here's a list of most popular ML interview questions and answers.
Simplilearn.com
Top 45 Machine Learning Interview Questions for 2025
Prepare for your machine learning interview with these top questions and answers. Boost your chances of landing the job with expert insights and tips.
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
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.
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.
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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
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
By DeepMind : https://lnkd.in/dd2VNhH
#ArtificialIntelligence #DeepLearning #ReinforcementLearning
lnkd.in
LinkedIn
This link will take you to a page that’s not on LinkedIn
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
lnkd.in
LinkedIn
This link will take you to a page that’s not on LinkedIn
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
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
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
Breathe for science
NYU study to help scale covid19 diagnosis over the phone. Simply record your breathing anonymously to participate.
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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
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
https://bit.ly/2Jt83T7
❇️ @AI_Python_EN
Medium
TensorFlow MLIR: An Introduction
Multi-level intermediate representation-a new compiler infrastructure
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
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
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
Matthewtancik
NeRF: Neural Radiance Fields
A method for synthesizing novel views of complex scenes by optimizing an underlying continuous volumetric scene function using a sparse set of input views.