ever wondered how we translate questions and commands into programs a machine can run? Jonathan Berant gives us an overview of (executable) semantic parsing.
#NLP

https://t.co/Mzvks7f9GR

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Here is a great explanation of how to combine Transformers and fastai to get great results from your NLP models
https://towardsdatascience.com/fastai-with-transformers-bert-roberta-xlnet-xlm-distilbert-4f41ee18ecb2

Free 81-page guide on learning #ComputerVision, #DeepLearning, and #OpenCV!

Includes step-by-step instructions on:
- Getting Started
- Face Applications
- Object Detection
- OCR
- Embedded/IoT
- ...and more

https://www.pyimagesearch.com/start-here

It should be really useful as according to this paper
https://arxiv.org/abs/1905.05583, the unsupervised finetuning and layer wise LR , and one-cycle are crucial for BERT performance. They mange to beat ULMFiT on IMDB with BERT-Base

A good introduction to #MachineLearning and its 4 approaches:

https://towardsdatascience.com/machine-learning-an-introduction-23b84d51e6d0?gi=10a5fcd4decd

#BigData #DataScience #AI #Algorithms #ReinforcementLearning

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Want to see how downstream results are affected by LSTM LM training configurations?

Save time/compute: use 125 pretrained LSTM LMs.

https://zenodo.org/record/3556943

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Yoshua explains how #DeepLearning has developed in 2019
https://www.youtube.com/watch?v=eKMA1Tscdag

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Depth-Aware Video Frame Interpolation (CVPR 2019)

https://www.youtube.com/watch?v=IK-Q3EcTnTA

DEBATE : Yoshua Bengio | Gary Marcus Pre-readings recommended to the audience before the Debate :
Yoshua Bengio | Gary Marcus

This Is The Debate The #AI World Has Been Waiting For

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Machine Learning Models

☞ https://morioh.com/p/1dc7518426c2

#TensorFlow #machinelearning

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nbdev: use Jupyter Notebooks for everything

https://www.fast.ai//2019/12/02/nbdev/

https://github.com/fastai/nbdev/

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Jupyter on Steroids: Create Packages, Tests, and Rich Documents https://t.co/w3K6D0Cgp6

Identifying Hate Speech with BERT and CNN
https://link.medium.com/7FaReCD781

Follow Us

https://www.linkedin.com/groups/13793807

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💡 What's the difference between bagging and boosting?

Bagging and boosting are both ensemble methods, meaning they combine many weak predictors to create a strong predictor.

One key difference is that bagging builds independent models in parallel and "averages" their results in the end, whereas boosting builds models sequentially, at each step emphasizing reducing error that remains in the model by better fitting to the observations that were missed in previous steps.

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Pre-Debate Material

“Yoshua Bengio, Revered Architect of AI, Has Some Ideas About What to Build Next”

The Turing Award winner wants AI systems that can reason, plan, and imagine

https://spectrum.ieee.org/tech-talk/robotics/artificial-intelligence/yoshua-bengio-revered-architect-of-ai-has-some-ideas-about-what-to-build-next

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Machine Learning in a company is 10% Data Science & 90% other challenges It's VERY hard. Everything in this guide is ON POINT, and it's stuff you won't learn in an ML book "Best Practices of ML Engineering" This is a lifesaver.
project:
https://martin.zinkevich.org/rules_of_ml/rules_of_ml.pdf

Very interesting use of #AI to tackle bias in the written text by substituting words automatically to more neutral wording. However, one must also consider the challenges and ramifications such technology could mean to the written language as it can not only accidentally change the meaning of what was written, it can also change the tone and expression of the author and neutralize the point-of-view and remove emotion from language.
#NLP
https://arxiv.org/pdf/1911.09709.pdf

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Named Entity Recognition Benchmark: spaCy, Flair, m-BERT and camemBERT on anonymizing French commercial legal cases

https://bit.ly/2rq1I5H
#DataScience #MachineLearning #ArtificialIntelligence #NLP

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"If the future can be different from the past and you don't have deep understanding, you should not rely on AI." - a rule from Ray Dalio for when to leverage machine learning for decision-making.

Full conversation:

❇️ @AI_Python_EN