quant-notes
Unverified ML strategy on Multi by dingran. BotFinder score 18 out of 100.
Quantitative Interview Preparation Guide, updated version here ==>
Source: github
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quant-notes
Preparations for DS/AI/ML/Quant What is this A short list of resources and topics covering the essential quantitative tools for data scientists, AI/machine learning practitioners, quant developers/researchers and those who are preparing to interview for these roles. At a high-level we can divide things into 3 main areas: 1. Machine Learning 2. Coding 3. Math (calculus, linear algebra, probability, etc) Depending on the type of roles, the emphasis can be quite different. For example, AI/ML interviews might go deeper into the latest deep learning models, while quant interviews might cast a wide net on various kinds of math puzzles. Interviews for research-oriented roles might be lighter on coding problems or at least emphasize on algorithms instead of software designs or tooling. List of resources A minimalist list of the best/most practical ones: Machine Learning: - Course on classic ML: Andrew Ng's CS229 (there are several different versions, the Cousera one is easily accessible. I used this older version) - Book on classic ML: Alpaydin's Intro to ML link - Course with a deep learing
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