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Archived reference fork. This copy is no longer maintained. Visit the original project for its current content and contribution guidance. For Android and AI learning, visit Android Engineers Academy.

About this fork: This repository is an Android Engineers fork of henrythe9th/AI-Crash-Course. Original content and attribution are preserved. Check the upstream repository for its current content and contribution guidance.

AI-Crash-Course

AI Crash Course to help busy builders catch up to the public frontier of AI research in 2 weeks

Intro: I’m Henry Shi and I started Super.com in 2016 and grew it to $150MM+ in annual revenues and recently exited. As a traditional software founder, I needed to quickly catch up to the frontier of AI research to figure out where the next opportunities and gaps were. I compiled a list of resources that were essential for me and should get you caught up within 2 weeks.

Start Here:
Neural Network -> LLM Series

Then get up to speed via Survey papers:

  • Follow the ideas in the survey paper that interest you and dig deeper

LLM Survey - 2024
Agent Survey - 2023
Prompt Engineering Survey - 2024

AI Papers: (prioritize ones with star *)

Foundational Modelling:
Transformers* (foundation, self-attention) - 2017
Scaling Laws/GPT3* (conviction to scale up GPT2/3/4) - 2020
LoRA (Fine tuning) - 2021
Training Compute-Optimal LLMs - 2022
RLHF* (InstructGPT->ChatGPT) - 2022
DPO (No need for RL/Reward model) - 2023
LLM-as-Judge (On par with human evaluations) - 2023
MoE (MIxture of Experts) - 2024

Planning/Reasoning:
AlphaZero/MuZero* (RL without prior knowledge of game or rules) - 2017/2019
CoT* (Chain of Thought)/ToT (Tree of Thoughts)/GoT (Graph of Thoughts) - 2022/2023/2023
ReACT (Generate reasoning traces and task-specific actions in interleaved manner) - 2022
Let’s Verify Step by Step (Process > Outcome) - 2023
ARC-Prize* (Latest methods for solving ARC-AGI problems) - 2024
Scaling Test-Time Compute (Relationship between inference-time and pre-training compute) - 2024

Applications:
Toolformer (LLMs to use tools) - 2023
GPT4 (Overview of GPT4, but fairly high level) - 2023
Llama3* (In depth details of how Meta built Llama3 and the various configurations and hyperparameters) - 2024
Gemini1.5 (Multimodal across 10MM context window) - 2024
Deepseekv3 (Building a frontier OSS model at a fraction of the cost of everyone else) - 2024
SWE-Agent/OpenHands (OpenSource software development agents) - 2024

Benchmarks:
SWE-Bench (Real world software development) - 2023
Chatbot Arena (Live human preference Elo ratings) - 2024


Videos/Lectures:
3Blue1Brown on Foundational Math/Concepts
Build a Large Language Model (from Scratch) #1 Bestseller
Andrej Kaparthy: Zero to Hero Series
Yannic Kilcher Paper Explanations
Noam Brown (o1 founder) on Planning in AI
Stanford: Building LLMs
Why You’re Not Too Old to Pivot Into AI (motivation)

Helpful Websites:
Full Stack Deep Learning - courses for building AI products
Prompting Guide - extensive list of prompting techniques and examples
a16z AI Cannon - similar list of resources, but longer (slightly dated)
2025 AI Engineer Reading List - longer reading list, broken out by focus area
State of Generative Models 2024 - good simple summary of current state

Others (non LLMs):
Vision Transformer (no need for CNNs) - 2021
Latent Diffusion (Text-to-Image) - 2021

Obvious/easy papers (to get your feet wet if you're new to papers):
CoT (Chain of Thought) - 2022
SELF-REFINE: Iterative Refinement with Self-Feedback - 2023

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Archived: Reference fork of henrythe9th/AI-Crash-Course. See upstream for the original AI research learning resources.

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