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oldsoldier123's Projects

algorithm_interview_notes-chinese icon algorithm_interview_notes-chinese

2018/2019/校招/春招/秋招/算法/机器学习(Machine Learning)/深度学习(Deep Learning)/自然语言处理(NLP)/C/C++/Python/面试笔记

alphapose icon alphapose

Real-Time Multi-Person Pose Estimation System

c-plus-plus icon c-plus-plus

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

computer-science icon computer-science

:mortar_board: Path to a free self-taught education in Computer Science!

cs228-material icon cs228-material

Teaching materials for the probabilistic graphical models and deep learning classes at Stanford

cs231n icon cs231n

斯坦福 cs231n 作业代码实践

cs231n-2017 icon cs231n-2017

Completed the CS231n 2017 spring assignments from Stanford university

deephash icon deephash

An Open-Source Package for Deep Learning to Hash (DeepHash)

deeplearning-500-questions icon deeplearning-500-questions

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

interview icon interview

📚 C/C++ 技术面试基础知识总结,包括语言、程序库、数据结构、算法、系统、网络、链接装载库等知识及面试经验、招聘、内推等信息。This repository is a summary of the basic knowledge of recruiting job seekers and beginners in the direction of C/C++ technology, including language, program library, data structure, algorithm, system, network, link loading library, interview experience, recruitment, recommendation, etc.

leedeeprl-notes icon leedeeprl-notes

李宏毅《深度强化学习》笔记,在线阅读地址:https://datawhalechina.github.io/leedeeprl-notes/

py-faster-rcnn icon py-faster-rcnn

Faster R-CNN (Python implementation) -- see https://github.com/ShaoqingRen/faster_rcnn for the official MATLAB version

python icon python

All Algorithms implemented in Python

pytorch icon pytorch

Tensors and Dynamic neural networks in Python with strong GPU acceleration

pytorch-metric-learning icon pytorch-metric-learning

The easiest way to use deep metric learning in your application. Modular, flexible, and extensible. Written in PyTorch.

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