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b7leung

MLE-Flashcards

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

TutorialsInterview guideML/AI fundamentals
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创建于 2022-07-23 · 更新于 2026-10-16 · 今日第 5022 名
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b7leung/MLE-Flashcards 排名第 5,022,有 2,516 个星标。

同类别比较

b7leung/MLE-Flashcards 排名第 5,022,此处有 8 个同类条目;blader/Claudeception 排名第 5,049。

同类别比较
名称排名指标
gordicaleksa/pytorch-GAT4,973
语言:
Jupyter Notebook
星标数:
2,687
分叉数:
357
简介:
My implementation of the original GAT paper (Veličković et al.). I've additionally included the playground.py file for visualizing the Cora dataset, GAT embeddings, an attention mechanism, and entropy histograms. I've supported both Cora (transductive) and PPI (inductive) examples!
Kayzaks/HackingNeuralNetworks4,989
语言:
Python
星标数:
2,641
分叉数:
306
简介:
A small course on exploiting and defending neural networks
amaas/stanford_dl_ex4,991
星标数:
2,626
分叉数:
1,575
简介:
Programming exercises for the Stanford Unsupervised Feature Learning and Deep Learning Tutorial
km1994/LLMs_interview_notes4,992
星标数:
2,623
分叉数:
170
简介:
该仓库主要记录 大模型(LLMs) 算法工程师相关的面试题
blader/Claudeception5,049
语言:
Shell
星标数:
2,428
分叉数:
32
简介:
A Claude Code skill for autonomous skill extraction and continuous learning. Have Claude Code get smarter as it works.
openai/openai-apps-sdk-examples5,070
语言:
TypeScript
星标数:
2,371
分叉数:
529
简介:
Example apps for the Apps SDK
bbruceyuan/LLMs-Zero-to-Hero5,091
语言:
Jupyter Notebook
星标数:
2,307
分叉数:
164
简介:
从无名小卒到大模型(LLM)大英雄~ 欢迎关注后续!!!
aws-samples/amazon-bedrock-workshop5,135
语言:
Jupyter Notebook
星标数:
2,216
分叉数:
961
简介:
This is a workshop designed for Amazon Bedrock a foundational model service.
关键参数与规格
关键参数与规格
参数值
类别Tutorials
子类别Interview guide,ML/AI fundamentals
星标数2,516
分叉数229
贡献者2
创建日期2022-07-23
更新日期2026-10-16
1 天星标数1
7 天星标数1
排名5,022
上次排名8,613
排名变化3,591
7 天星标排名9,353
上次 7 天星标排名12,074
7 天星标排名变化2,721
总星标排名5,248
上次总星标排名5,237
总星标排名变化-11
README 摘录

Machine Learning Flashcards 250+ flashcards I made as an exercise & reference for myself, after from years of ML research, coursework, & independent study. Hopefully other people can benefit from them as well, for study or interview prep! Topics covered includes: **computer science, classical ML, modern deep learning, 2D/3D computer vision, NLP, reinforcement learning, generative models**. Intended Scope and Audience These flashcards generally assume a good foundation in these topics, and a lot of technical terminology is used. There may be

errors in these slides, or things that I've missed. Switched to powerpoint due to better equation editing.

常见问题

记录了多少星标和分叉?

b7leung/MLE-Flashcards 有 2,516 个星标和 229 个分叉。

仓库排名是多少?

b7leung/MLE-Flashcards 排名第 5,022。

哪个仓库排名最接近?

b7leung/MLE-Flashcards 与 blader/Claudeception 比较,后者排名第 5,049,有 2,428 个星标。

README

Machine Learning Flashcards

250+ flashcards I made as an exercise & reference for myself, after from years of ML research, coursework, & independent study. Hopefully other people can benefit from them as well, for study or interview prep!

Topics covered includes: computer science, classical ML, modern deep learning, 2D/3D computer vision, NLP, reinforcement learning, generative models.

Intended Scope and Audience

These flashcards generally assume a good foundation in these topics, and a lot of technical terminology is used. Potential approaches may differ depending on your current experience:

  • Already have a good foundation in ML: you can probably use them as-is to review and fill in any missing knowledge gaps

  • Newer to ML, this may provide a good overview of what is out there, and I'd suggest also refering to other materials focused on education & learning (see "additional links" below)

Note that some topics are covered more comprehensively/accurately than others -- and because the field is constantly changing, this is not meant to be a definitive resource. There may be errors in these slides, or things that I've missed. If so, feel free let me know!

Changelog

  • May 2025 -- Updated with topics in RL, NeRFs, gaussian splatting, generative models, LLMs, and VLMs. Switched to powerpoint due to better equation editing.
  • July 2022 -- Initial set of flashcards.

Additional Links & Resources

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