AllenWrong
A research engineer dedicated to solving practical problems using mathematics and computers. But never limiting himself.
Beijing
AllenWrong's Stars
public-apis/public-apis
A collective list of free APIs
kamranahmedse/developer-roadmap
Interactive roadmaps, guides and other educational content to help developers grow in their careers.
practical-tutorials/project-based-learning
Curated list of project-based tutorials
jlevy/the-art-of-command-line
Master the command line, in one page
papers-we-love/papers-we-love
Papers from the computer science community to read and discuss.
MunGell/awesome-for-beginners
A list of awesome beginners-friendly projects.
satwikkansal/wtfpython
What the f*ck Python? 😱
huihut/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.
kenjihiranabe/The-Art-of-Linear-Algebra
Graphic notes on Gilbert Strang's "Linear Algebra for Everyone"
Hannibal046/Awesome-LLM
Awesome-LLM: a curated list of Large Language Model
freeCodeCamp/how-to-contribute-to-open-source
A guide to contributing to open source
roboticcam/machine-learning-notes
My continuously updated Machine Learning, Probabilistic Models and Deep Learning notes and demos (2000+ slides) 我不间断更新的机器学习,概率模型和深度学习的讲义(2000+页)和视频链接
jessevig/bertviz
BertViz: Visualize Attention in NLP Models (BERT, GPT2, BART, etc.)
amitness/learning
A log of things I'm learning
WooooDyy/LLM-Agent-Paper-List
The paper list of the 86-page paper "The Rise and Potential of Large Language Model Based Agents: A Survey" by Zhiheng Xi et al.
nvim-orgmode/orgmode
Orgmode clone written in Lua for Neovim 0.9+.
Fewbytes/rubber-docker
A workshop on Linux containers: Rebuild Docker from Scratch
Paitesanshi/LLM-Agent-Survey
filipecalegario/awesome-generative-ai
A curated list of Generative AI tools, works, models, and references
Doragd/Algorithm-Practice-in-Industry
搜索、推荐、广告、用增等工业界实践文章收集(来源:知乎、Datafuntalk、技术公众号)
dair-ai/Transformers-Recipe
🧠 A study guide to learn about Transformers
tangxyw/RecSysPapers
推荐/广告/搜索领域工业界经典以及最前沿论文集合。A collection of industry classics and cutting-edge papers in the field of recommendation/advertising/search.
ashishpatel26/Amazing-Feature-Engineering
Feature engineering is the process of using domain knowledge to extract features from raw data via data mining techniques. These features can be used to improve the performance of machine learning algorithms. Feature engineering can be considered as applied machine learning itself.
LeoGrin/tabular-benchmark
ckaestne/seai
CMU Lecture: Machine Learning In Production / AI Engineering / Software Engineering for AI-Enabled Systems (SE4AI)
DrugowitschLab/ML-from-scratch-seminar
This repository is part of a "Machine Learning from Scratch" seminar at Harvard Medical School.
tiagoantao/python-performance
Repository for the book Fast Python - published by Manning
DanielTrosten/DeepMVC
chenxi1103/Netflix-Movie-Recommendation-System
Developed a full-stack movie recommendation system with RESTful API to provide clients with 20 recommended movies. Determine the hit rate by collecting clients’ watching data to see if they watch the recommended movies afterwards. Continuous Integration for pipeline code. Automated daily model quality evaluation and system supervision with Jinkens. Designed and built the infrastructure that can incrementally deploy new versions of recommendation service triggered by canary release and A/B testing. Integrated with feedback loops mechanism to detect potential positive or negative feedback loops to further identify potential adversarial attacks. Implemented the monitoring and detection by applying lambda architecture to combine the stream and batch processing results to detect problematic behaviors. Comprehensive data quality control on raw data received from Kafka stream, especially focus on data schema issues, missing data, and duplicated data. Monitoring Dashboard UI with D3.js. Developed the whole web server by Flask. Containerized the whole service by Docker.
asleepyfish/wx-pusher
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