How Does NLP Benefit Legal System: A Summary of Legal Artificial Intelligence

https://github.com/thunlp/LegalPapers

Paper:
https://arxiv.org/abs/2004.12158v2

Jukebox: a new generative model for audio from OpenAI.

Jukebox, a model that generates music with singing in the raw audio domain.

openai.com/blog/jukebox

Article: cdn.openai.com/papers/jukebox.pdf

Examples: https://jukebox.openai.com/

Code: https://github.com/openai/jukebox

Reinforcement Learning with Augmented Data

https://mishalaskin.github.io/rad

Code: https://github.com/MishaLaskin/rad

Paper: https://arxiv.org/abs/2004.14990

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

🗣 @AI_Python_arXiv
✴️ @AI_Python_EN
❇️ @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.

🗣 @AI_Python_arXiv
✴️ @AI_Python_EN
❇️ @AI_Python

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

🗣 @AI_Python_arXiv
✴️ @AI_Python_EN
❇️ @AI_Python

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/

🗣 @AI_Python_arXiv
✴️ @AI_Python_EN
❇️ @AI_Python

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

🗣 @AI_Python_arXiv
✴️ @AI_Python_EN
❇️ @AI_Python

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.


🗣 @AI_Python_arXiv
✴️ @AI_Python_EN
❇️ @AI_Python