Popular GitHub Repositories for Placement Preparation

Collection of various algorithms in mathematics, machine learning, computer science and physics implemented in C++ for educational purposes.

This repo is meant to serve as a guide for Machine Learning/AI technical interviews.

AI算法工程师面试宝典：三年面试经验，五年模拟题库！本书涵盖AIGC、传统深度学习、自动驾驶、机器学习、计算机视觉、自然语言处理、强化学习、具身智能、元宇宙、AGI等人工智能领域的面试笔试真题及实用技巧，助你轻松应对AI行业挑战。

200+ detailed flashcards useful for reviewing topics in machine learning, computer vision, and computer science.

Prepping tables for machine learning

【三年面试五年模拟】AI算法工程师面试秘籍。涵盖AIGC、传统深度学习、自动驾驶、机器学习、计算机视觉、自然语言处理、强化学习、具身智能、元宇宙、AGI等AI行业面试笔试经验与干货知识。

LLMs interview notes and answers:该仓库主要记录大模型（LLMs）算法工程师相关的面试题和参考答案

Deep learning toolkit-enabled VLSI placement

Scalable data pre processing and curation toolkit for LLMs

Open source project for data preparation of LLM application builders

[Pedestron] Generalizable Pedestrian Detection: The Elephant In The Room. @ CVPR2021

Data Preparation for Satellite Machine Learning

Continuously updated paper list on advancements in Data Agents. Companion repo to our paper "A Survey of Data Agents: Emerging Paradigm or Overstated Hype?"

? Pytorch interview questions and answers to help you prepare for your next machine learning and data science interview in 2025.

This repo is meant to serve as a detailed guide for Machine Learning/AI interviews.

VerticaPy is a Python library that exposes sci-kit like functionality to conduct data science projects on data stored in Vertica, thus taking advantage Vertica’s speed and built-in analytics and machine learning capabilities.

An open source book to learn data science, data analysis and machine learning, suitable for all ages!

Master Thesis: Limit order placement with Reinforcement Learning

?️ Blueprint-driven Claude Code autopilot. Smart preparation over endless iterations - AI analyzes patterns, creates solid implementation plans, delivers working code in 15 minutes.

A tool for precisely placing 3D landmarks on 3D facial scans based on the paper "Multi-view Consensus CNN for 3D Facial Landmark Placement"