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
If you're interested in using @pytorch on free Colab TPUs, here are some notebooks to get you started
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: https://t.co/TE80t3iykH
▶️ $ streamlit run streamlit_spacy.py