This tweet is about how I have studied ML and made it my profession. I'll share the resources I've used and the sequence of my study. Straight to point ML pre-requisites(maths) : Linear Algebra, Probability Theory, Calculus, Optimization Theory(optional), Information theory(optional) Linear Algebra: Lecture course by Gilbert Strang Probability theory: MIT 6.041 (it contains parts of Bayesian inference as well) Calculus: your high school and college classes are enough Once basic maths is done then we move to ML. Classical ML : CS229. Either by Andrew NG or someone else. Follow their lecture notes and solve their problem sets. Reference books for classical ML that I followed: PRML by Christopher bishop, Pattern Classification by Duda, Hart and Stork After getting comfortable with classical ML we move to Deep Learning and everything else. Deep Learning and Computer Vision: CS231n. Very good lecture and assignments Reference book: Deep Learning by Ian Goodfellow. This is the best book on deep learning. I’ve read some chapters of it many many times. Beautiful maths and intuitions MLOps: dvc, WandB, MLFlow NLP: I just read hugging face blogs. I haven’t spent much time with classical NLP though. Alignment/AI safety/AI explainability: Anthropic Blogs(I’m a noob in this, just started learning couple months ago) Additionally: Blogs: Lilian Weng(OpenAI)’s blogs, colah’s blogs Additionally: arxiv. I read many papers from arxiv Karas and Tensorflow blogs: for introductory code about modern deep learning frameworks Competitions: Kaggle Cloud compute. GCP/collab/Kaggle notebooks PS: this is not a roadmap. Just what I followed till now and I find it quite structured. Even after 5 years I still find myself learning new stuff everyday.

Ramanujan level stuff

what in the actual fuck???

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How do we build trust ladder for memory? AI writes to slack, to notion, to JIRA and then reads back from it, considering it the source of truth? One misleading fact and it becomes impossible to spot it unless it's too late Coding on the other can provide immediate feedback

> leaves Anthropic > immediately starts slandering Opus

diogo knows ball

hmmmmm

Claudia dropped another banger on me Wat dat mean 😭😭

>India isn't developed, it's developing, so it needs patriots, not parasites Preach

"Is it still worth pursuing ML as a career"? Honestly this might be the best time. But you've to be careful Just like you can't approach software enineering like you used to 7 years ago you can't approach ML the same way as well Fundamentsl like Linear Algebra, Probability and Calculus is always needed, but once that's done, you gotta pick up a niche and start building on the go Machine learning basics? Start with MNIST Computer Vision? Start with Yolo NLP? Start with sentiment analysis with encoder models Generation? Start with VAEs and GANs Text generation? Start with GPTs Content summarization? Start with BERT The way I learnt ML 10 years ago and the way ML should be learnt right is different. Every other day, 100s of new research papers on a topic is being published; the scope and much more broader and tuning knobs are becoming infinite Pick a niche, and get started

RAG is one of the most used B2B enterprise solutions right now

This is so real