Resources I used for ML Engineer, Applied Scientist and Quant Researcher interviews.
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Updated
Feb 4, 2022
Resources I used for ML Engineer, Applied Scientist and Quant Researcher interviews.
A comprehensive, well-structured repository of B.Tech (Hons) CSE notes and learning resources, specializing in Artificial Intelligence and Data Science. Includes semester-wise notes, question papers, curated study guides, and indexed materials designed for efficient learning, revision, and academic reference.
Neural parameter calibration for multi-agent models. Uses neural networks to estimate marginal densities on parameters and networks
🎓💻All of my projects at University of Tehran
Pandas + Bayesian Statistics - to see if left-handed people actually die earlier than righties.
PySATL Core Library for computation over probablity distribution, and parametric families
A compilation of useful resources (I use Obsidian). Create a PR if you want to contribute!
微信公众号:人工智能大讲堂,专注人工智能底层数学原理与应用,专栏包括线性代数,概率统计,机器学习,深度学习
Using regression discontinuity try to see which debts are worth collecting.
Repo of HackerRank Probability & Statistics - Foundations Challenges
Statistics Cheatsheets
Essential Books for Computer Science
This Repository is Containing all the work I have done in my 3rd Semester of Btech in JKLU
This repository contains my coursework project for ECS7005P - Risk and Decision-Making for Data Science and AI. It applies probabilistic models, Bayesian networks, and decision analysis using Python and PyAgrum to evaluate risk and optimise decision-making under uncertainty.
This GitHub repository Consists of materials, code samples, documentation, and valuable resources related to the Information Technology (IT) Department at the National Institute of Technology Karnataka (NITK). 📚 Resource Library 💻 Code Samples 🗂️ Project Repositories
Formula Sheet for Ph.D Qualifying Exams in Probability and Stochastic Processes
Mathematics for Machine Leaning
This repository contains guided projects from Dataquest's Data Analysis in Python path.
微信公众号:人工智能大讲堂,专注人工智能底层数学原理与应用,专栏包括线性代数,概率统计,机器学习,深度学习
The fifth project from a Data Scientist with Python track by DataCamp
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