Conversation with Gilbert Strang, a professor of mathematics at MIT & an inspiring teacher of linear algebra to millions of students around the world through MIT OpenCourseWare.

https://www.youtube.com/watch?v=lEZPfmGCEk0

❇️ @AI_Python_EN

Another nice visual guide by Jay Alammar about how you can use BERT to do text classification. In particular, he’s using DistilBERT to create sentence embeddings which is then used as an input for logistic regression. Code is also provided! Check it out! #deeplearning #machinelearning #NLP
📝 Article:
https://jalammar.github.io/a-visual-guide-to-using-bert-for-the-first-time/

❇️ @AI_Python_EN

Machine Learning From Scratch

https://github.com/eriklindernoren/ML-From-Scratch
#ArtificialIntelligence #DeepLearning #MachineLearning

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You can now easily deploy your TensorFlow models in a Google Colab or Jupyter notebook with TensorFlow Extended (TFX)! Very nice! Check out the article and the tutorial for more details.
#deeplearning #machinelearning

📝 Article:
https://blog.tensorflow.org/2019/11/introducing-tfx-interactive-notebook.html?m=1

❇️ @AI_Python_EN

Countdown to NeurIPS 2019 continues... (5th of 8 studies my team will present) A lot of companies ranging from small startups to large corporate giants are releasing Explainable AI toolkits and core features using popular XAI methods like LIME, SHAP, Integrated Gradients, etc. However, one begs the question: Do the explanations provided by these XAI methods really reflect the decisions made by machine learning algorithm? In this study, we introduce a measurable way in an attempt to answer this question, and study some of the most popular XAI methods to see where they stand for deep neural networks. The results may surprise you...
#deeplearning #neurips
https://arxiv.org/abs/1910.07387

❇️ @AI_Python_EN

4 Traits, qualities that a data scientist must seek ...

1) Technical bar: Data science teams work everyday in SQL, specifically in Postgres, and expect candidates to know Python/some fluency in some sort of statistical language. Also, someone who is really comfortable with querying really large datasets.

2) Communication: we’re in roles where a lot of our day-to-day is spent getting great insights or building models and communicating results of that to stakeholders, whether that’s product managers, marketing folks or finance. It’s super key that data science candidates have good communication skills.

3) Grit, tenacity and willingness to solve hard problems: Things that DS teams solve are generally hard problems. My hope is that anyone who joins the data science team is excited about hard problems and bumping against hard challenges.

4) Passion for the arts and passion for the mission: This is not the most important but great to have.
#datascience

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Vanishing/exploring gradients problem is a well often problem especially when training big networks, so visualizing gradients is a must when training neural networks. Here is the small network's on MNIST dataset gradients flow. A detailed article is on the way to explain many things in deep learning.

#machinelearning #deeplearning #artificialintelligence #computervision #neuralnetwork

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FacebookAI: Is the lottery ticket phenomenon a general property of DNNs or merely an artifact of supervised image classification? We show that the lottery ticket phenomenon is a general property which is present in both
#reinforcementlearning #NLP

https://arxiv.org/abs/1906.02768

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The Mind at Work: Guido van Rossum on how Python makes thinking in code easier

https://blog.dropbox.com/topics/work-culture/-the-mind-at-work--guido-van-rossum-on-how-python-makes-thinking

#Python

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New State of the Art AI Optimizer: Rectified Adam (RAdam) Improve your AI accuracy instantly versus Adam, and why it works. Blog by Less Wright :

https://medium.com/@lessw/new-state-of-the-art-ai-optimizer-rectified-adam-radam-5d854730807b

#MachineLearning #TensorFlow #Pytorch #DeepLearning

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Functional #RL with #Keras and #Tensorflow Eager

https://bit.ly/2IIyzIj

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#GraphNeuralNetwork s for Natural Language Processing

#neuralnetwork #NLP

https://bit.ly/33oprRc

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As it turns out, Wang Ling was way ahead of the curve re NLP's muppet craze (see slides from LxMLS '16 & Oxford #NLP course '17 below).


https://github.com/oxford-cs-deepnlp-2017/lectures

❇️ @AI_Python_EN

Transformers v2.2 is out, with *4* new models and seq2seq capabilities!

ALBERT is released alongside CamemBERT, implemented by the authors, DistilRoBERTa (twice as fast as RoBERTa-base!) and GPT-2 XL!

Encoder-decoder with
⭐Model2Model⭐

Available on

https://github.com/huggingface/transformers/releases/tag/v2.2.0

#NLP

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📢📢📢 Twitter Cortex is creating a NLP Research team. Brand new #NLP Researcher💫 job posting👇 Please spread the word.
https://careers.twitter.com/en/work-for-twitter/201911/machine-learning-researcher-nlp-cortex-applied-machine-learning.html

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Single Headed Attention RNN: Stop Thinking With Your Head

https://arxiv.org/abs/1911.11423

#ArtificialIntelligence #NeuralComputing #NLP


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Lit BERT: NLP Transfer Learning In 3 Steps Blog by William Falcon :
https://towardsdatascience.com/lit-bert-nlp-transfer-learning-in-3-steps-272a866570db


#MachineLearning #ArtificialIntelligence #NLP

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Microsoft: Actor critic method bests greedy exploration in #reinforcementlearning

http://bit.ly/2sfxt17

#DataScience #MachineLearning #ArtificialIntelligence

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Is Machine Learning Really AI?

https://www.forbes.com/sites/cognitiveworld/2019/11/21/is-machine-learning-really-ai/

#InternetOfThings #iot #machinelearning #artificialintelligence

❇️ @AI_Python_EN

In #datascience, you must understand context. There are times at work where looking at the data alone didn't help me from solving the problem.

It doesn't matter if your domain is in marketing, healthcare, product, etc... You need to understand the context first before diving into the data. Without background information about how the data was generated, it becomes really difficult to make accurate assumptions on what your data will show.

Taking the time to understand the context will not only benefit you in your analysis, but you may even help your colleagues tackle the problem better.

When you are informed about the data and problem, you increase your value because now you're in a position to communicate and identify other potential problems.

So do this:
On your next project, take the time to not just do EDA, but also document your understanding of the context behind the data.

This good practice will definitely help you in your career and is a valuable skill you can bring to any team.
Context first, data second.

❇️ @AI_Python_EN