An NLU-Powered Tool to Explore COVID-19 Scientific Literature

https://ai.googleblog.com/2020/05/an-nlu-powered-tool-to-explore-covid-19.html

Beneath the Tip of the Iceberg: Current Challenges and New Directions in Sentiment Analysis Research

Awesome Sentiment Analysis papers: https://github.com/declare-lab/awesome-sentiment-analysis

Paper: https://arxiv.org/abs/2005.00357v1

Set of Machine Learning Python plugins for GIMP


Github: https://github.com/kritiksoman/GIMP-ML

Paper: https://arxiv.org/abs/2004.13060

Demo: https://www.youtube.com/watch?v=HVwISLRow_0

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❇️ @AI_Python

How to Create a Web API using Flask and Python

https://www.i2tutorials.com/how-to-create-a-web-api-using-flask-and-python/

Announcing Meta-Dataset: A Dataset of Datasets for Few-Shot Learning

https://ai.googleblog.com/2020/05/announcing-meta-dataset-dataset-of.html

Statistical Imputation for Missing Values in Machine Learning

https://machinelearningmastery.com/statistical-imputation-for-missing-values-in-machine-learning/

New Data Scientists - When you learn, it's easy to get distracted by Machine Learning & Deep Learning terms like "XGBoost", "Neural Networks", "RNN", "LSTM" or Advanced Technologies like "Spark", "Julia", "Scala", "Go", etc.

Don't get bogged down trying to learn every new term & technology you come across.

Instead, focus on foundations.
- data wrangling
- visualizing
- exploring
- modeling
- understanding the results.


Build yourself up. You'll advance much faster.

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XCOPA, a dataset for commonsense reasoning and knowledge transfer across 11 languages (including Quechua and Haitian Creole).

Download:
http://github.com/cambridgeltl/xcopa
Paper:
http://ducdauge.github.io/files/xcopa.pdf

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Introducing Jukebox, a neural net that generates music, including rudimentary singing, as raw audio in a variety of genres and artist styles. We're releasing a tool for everyone to explore the generated samples, as well as the model and code: https://openai.com/blog/jukebox/

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Free Live Course: Deep Learning with PyTorch

https://www.freecodecamp.org/news/free-deep-learning-with-pytorch-live-course/

video: https://www.youtube.com/watch?v=vo_fUOk-IKk

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Introduction to Neural Networks in Python | Tensorflow/Keras

https://morioh.com/p/abae4875bdd2/

SymJAX: symbolic CPU/GPU/TPU programming.

docs: https://symjax.readthedocs.io/en/latest/

github: https://github.com/RandallBalestriero/SymJAX

pdf: https://arxiv.org/pdf/2005.10635v1.pdf

Evaluating Natural Language Generation with BLEURT

BLEURT (Bilingual Evaluation Understudy with Representations from Transformers)

https://ai.googleblog.com/2020/05/evaluating-natural-language-generation.html

Github: https://github.com/google-research/bleurt

Paper: https://arxiv.org/abs/2004.04696

Tensorflow vs PyTorch for Text Classification using GRU

https://medium.com/@rodolfosaldanha_71881/tensorflow-vs-pytorch-for-text-classification-using-gru-e95f1b68fa2d

CVML web lecture series on basics of deep learning. Registration required (June 3rd)
Deep Learning: Convolutional Neural Networks
Deep Object Detection

http://icarus.csd.auth.gr/cvml-web-lecture-series/

Andriy Burkov :
If you plan to do your Master's or Ph.D., choose your research advisor carefully. Ask about them his* current and past students, whether he was easily available for them, whether he responded to the requests for reference sent by their employers.

My research advisor, when was asked by one of my first employers whether they should hire me, responded: "You will call me back to thank me if you hire him." This is what a great research advisor would do for his students.

Unfortunately, not all research advisors are like this. For one of my hires, their research advisor didn't respond to two of my emails with requests for reference. I still hired the candidate, and I'm very happy with my choice. But the research advisor should be ashamed to ignore such requests about their recent alumni. To be available for their present and past students is advisors' direct duty.


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Adrian Olszewski :
EDIT: find the answer here: tinyurl.com/yc4m5lxx )

Dear Data Scientists. ANOVA is a powerful method. You often mention it in your posts. Sadly, I noticed, that you treat it mostly in the simplest way, while it's far beyond that! Well, Fisher didn't invent it with all those applications in mind, but it turned out, over time, that the procedure can be generalized greatly, constituting one of the most important methods in statistics.

You think you know all about it? ANOVA may surprise you.

You might have wondered, why:
- is ANOVA called in so many contexts: to compare means, models, testing contrasts?
- why is it called with either F or chi2 test (yes, it's about limiting distribution, but how?)
- why is it important to call it with appropriate type of sum of squares (when)?
- what is the relationship with LS-Means
- what does "joint test" actually means.

You might believe that the Tukey HSD method must agree with the result of F test in ANOVA. / No, it doesn't. Scheffe's does. /

If you pick the right answer, then do a research, you will understand how deep is the rabbit hole.

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NeuralPy

NeuralPy: A Keras like deep learning library works on top of PyTorch PyTorch is an open-source machine learning framework that accelerates the path from research prototyping to production deployment developed by Facebook runs on both CPU and GPU.

Github: https://github.com/imdeepmind/NeuralPy

Project: https://neuralpy.imdeepmind.com/

Acme: A new framework for distributed reinforcement learning by DeepMind
Intro:
https://deepmind.com/research/publications/Acme
Paper:
https://github.com/deepmind/acme/blob/master/paper.pdf
Repo:
https://github.com/deepmind/acme

#reinforcementlearning #ai #deepmind #deeplearning #machinelearning

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