I created a little resource to help one practice their deep learning skills!

Only 1st lecture of part 2 v3 for now but maybe there will be more 🙂

Machine learning portfolio tips

1. Good ideas come from ML sources that are a bit quirky.

- NeurIPS from 1987 - 1997
- Stanford’s CS224n & CS231n projects
- Twitter likes from ML outliers
- ML Reddit’s WAYR
- Kaggle Kernels
- Top 15-40% papers on Arxiv Sanity

Interesting study on the Kids These Days effect, or why the youth of today seem lacking; it offers insights into more than one bias

An exercise for an age of reactivity:

Spend a week writing down everything that bothers you. Outrages in the news, personal slights, daily irritations.

Read it a month later. How many entries feel important now? Probably not many.

Excess reactivity steals happiness and energy.

Who said that training GPT-2 or BERT was expensive?

"We use 512 Nvidia V100 GPUs [...] Upon the submission of this paper, training has lasted for three months [...] and perplexity on the development set is still dropping."

[email protected] is very cool 🔥✨

It took me hardly any time and around 100 lines of Python to build an interactive @spacy_io model visualizer, complete with dependencies, named entities, similarity and more.

📄 Code:
▶️ $ streamlit run

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