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Hi there,
A quick Monday note about our Twitter :)
We share lots of helpful resources for your data science and ML journey. Free courses:
CS109A Data Science course materials @Harvard are free and open for everyone!
1. Lecture notes
2. R code, Python notebooks
3. Lab material
4. Advanced sections
Learn here: harvard-iacs.github.io/2019-CS109A/pa…
and books:
A free book for you!
Fundamentals of Data Visualization by Claus O. Wilke
It's a guide to making visualizations that accurately reflect the data, tell a story, and look professional.
Read the open book here: clauswilke.com/dataviz/
and helpful lists:
5 self-supervised learning models, you should know about:
1. Wav2vec
2. BYOL
3. XLM-R
4. AVID
5. SEER
Find links and descriptions here: thesequence.substack.com/p/-edge137-sel…
As well as our favorite ‘ML Research of the Week’:
ML Research of this week:
1. @DeepMind AlphaFold DB vNext
2. @GoogleAI ML Code Completion
3. @AmazonScience Causal Inference and ML
Read more here: thesequence.substack.com/p/-deepminds-a…
If you haven’t yet, please help us shape the ML value chain landscape
Let’s create an objective landscape of the ML Value Chain together. You’ve probably seen some AI/ML companies’ landscapes before. They are typically assembled by either analyst firms, media, or VC firms. But we trust that TheSequence’s audience only can shape an accurate landscape of the ML Value Chain. Participate and be the first to receive this super helpful research shaped by you!
Many thanks 🧠