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mli/paper-reading

★ 33,286Audience · researcherComplexity · 1/5StaleLicenseSetup · easy

TLDR

Curated video lectures explaining landmark deep learning research papers line-by-line, with links to original papers and recorded walkthroughs on YouTube and Bilibili.

Mindmap

mindmap
  root((repo))
    What it does
      Video lectures
      Paper explanations
      Research walkthroughs
    Papers covered
      GPT and LLMs
      Vision models
      Multimodal AI
    Learning format
      Chronological table
      Original paper links
      Bilibili and YouTube
    Audience
      ML practitioners
      Researchers
      Students

Things people build with this

USE CASE 1

Watch guided video explanations of GPT-4, Llama, and other landmark AI papers to understand their core ideas without reading dense academic writing.

USE CASE 2

Build a structured learning path through deep learning history by following the chronological table of papers with video walkthroughs.

USE CASE 3

Reference original papers and instructor explanations together to deepen understanding of techniques like Chain of Thought, CLIP, and InstructGPT.

Getting it running

Difficulty · easy Time to first run · 5min
Use freely for any purpose including commercial. Keep the notice and disclose changes to the patent grant.

In plain English

This repository is a curated reading and video resource for deep learning research papers. The primary language in the README is Chinese, and the description translates roughly to "line-by-line close reading of classic and new deep learning papers." The core content is a chronological table listing important AI and deep learning research papers alongside recorded video walkthroughs where an instructor reads and explains each paper in detail. The papers covered are landmark works in the field, including foundational models and techniques like GPT-4, Llama 3.1, OpenAI's Whisper (speech recognition model), InstructGPT (the technique behind instruction-following AI assistants), Chain of Thought prompting, CLIP, DALL-E, and many others spanning natural language processing, computer vision, and multimodal AI. Each entry links to the original paper and to video recordings on Bilibili (a Chinese video platform) and YouTube, along with view counts. The README does not describe code or software tools. This is an educational video lecture series organized as a GitHub repository. The instructor reads through each paper section by section, explaining the motivation, methodology, and significance, making dense academic research accessible to practitioners who want to understand the ideas without needing to fully parse academic writing on their own. You would use this resource if you are a machine learning practitioner, researcher, or student who wants guided walkthroughs of important deep learning papers, particularly if you are comfortable reading Chinese or watching lectures in Chinese. The repository has no primary programming language because it contains no source code, it is purely a reading list and video index. It is a widely cited community learning resource in the Chinese AI research community.

Copy-paste prompts

Prompt 1
I want to understand the GPT-4 paper but find it hard to read. Can you explain the key ideas from the mli/paper-reading video lecture on it?
Prompt 2
What are the most important deep learning papers I should study? Show me the chronological list from mli/paper-reading and help me prioritize.
Prompt 3
Walk me through the CLIP paper using the explanation style from mli/paper-reading, what problem does it solve and how?
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