18 Lessons, Get Started Building with Generative AI 🔗 https://microsoft.github.io/generative-ai-for-beginners/

🔥 Linux下C++轻量级Web服务器学习

🦀️ Re-implement xv6-riscv in Rust

Example notebooks that show how to apply machine learning, deep learning and reinforcement learning in Amazon SageMaker

Learn how to design large-scale systems. Prep for the system design interview.

A curated list of awesome Amazon Web Services (AWS) libraries, open source repos, guides, blogs, and other resources. Featuring the Fiery Meter of AWSome.

cpp implementation of robotics algorithms including localization, mapping, SLAM, path planning and control

This repository contains the solutions and explanations to the algorithm problems on LeetCode. Only medium or above are included. All are written in C++/Python and implemented by myself. The proble…

Apollo notes (Apollo学习笔记) - Apollo learning notes for beginners.

A self driving car course of University of Toronto, on the topic of state estimation and localization

A cheatsheet of modern C++ language and library features.

《统计学习方法》的代码实现

notes of machine learning algorithm derivation

My continuously updated Machine Learning, Probabilistic Models and Deep Learning notes and demos (1500+ slides) 我不间断更新的机器学习，概率模型和深度学习的讲义(1500+页)和视频链接

Starter project for easy learning and use of ApprovalTests.cpp

PRML algorithms implemented in Python

Statistical learning methods, 统计学习方法(第2版)[李航] [笔记, 代码, notebook, 参考文献, Errata, lihang]

Collection of papers and other resources for object tracking and detection using deep learning

Semantic Segmentation Suite in TensorFlow. Implement, train, and test new Semantic Segmentation models easily!

The C++ Core Guidelines are a set of tried-and-true guidelines, rules, and best practices about coding in C++

This is a repository containing the list of company wise questions available on leetcode premium

深度学习500问，以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述，以帮助自己及有需要的读者。 全书分为18个章节，50余万字。由于水平有限，书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作，联系[email protected] 版权所有，违权必究 Tan 2018.06

Jupyter notebooks for the code samples of the book "Deep Learning with Python"