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A curated list of resources tailored towards AI Engineers
Stanford Online's Playlists (look for AI collections):
Stanford CS229 Links
Stanford CS25 Links
| Year | Paper Title | Publication Details | Link |
|---|---|---|---|
| 1993 | Keeping Neural Networks Simple by Minimizing the Description Length of the Weights | NIPS 1993 | |
| 2004 | A Tutorial Introduction to the Minimum Description Length Principle | Online Publication | arXiv |
| 2008 | Machine Super Intelligence | PhD Thesis | Google Drive |
| 2011 | The First Law of Complexodynamics | Blog Post | scottaaronson.blog |
| 2012 | ImageNet Classification with Deep Convolutional Neural Networks | NIPS | |
| 2014 | Quantifying the Rise and Fall of Complexity in Closed Systems: The Coffee Automaton | arXiv | arXiv |
| 2014 | Neural Turing Machines | arXiv | arXiv |
| 2015 | Recurrent Neural Network Regularization | arXiv | arXiv |
| 2015 | The Unreasonable Effectiveness of Recurrent Neural Networks |
I created a NotebookLM audio overview of these papers. Remember that it's AI generated and not a full substitute for reading and understanding the studies:
| Year | Paper Title | Publication Details | Link |
|---|---|---|---|
| 2025 | The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity (Apple) | arXiv | arXiv |
| 2025 | The Illusion of the Illusion of Thinking (Anthropic) | arXiv | arXiv |
| Year | Paper Title | Link | GitHub (if found) |
|---|---|---|---|
| 2020 | T5 | GitHub | |
| 2020 | GPT‑3 | N/A | |
| 2020 | RAG | GitHub | |
| 2022 | Chain-of-Thought Prompting | N/A | |
| 2022 | Constitutional AI | GitHub | |
| 2023 | Gorilla: Large Language Model Connected with Massive APIs | arXiv | N/A |
| 2023 | GPT‑4 Technical Report | N/A | |
| 2023 | Llama 2 | GitHub | |
| 2023 | Instruction Tuning Survey |
Selected from shared topics, language and repository description—not editorial ratings.
SamurAIGPT /
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weitianxin /
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SamurAIGPT /
| Blog Post |
| karpathy.github.io |
| 2015 | Pointer Networks | arXiv |
| 2015 | Understanding LSTM Networks | Blog Post |
| 2015 | Deep Speech 2: End-to-End Speech Recognition in English and Mandarin | PMLR |
| 2015 | Deep Residual Learning for Image Recognition | arXiv | arXiv |
| 2016 | Order Matters: Sequence to Sequence for Sets | arXiv | arXiv |
| 2016 | Multi-Scale Context Aggregation by Dilated Convolutions | arXiv | arXiv |
| 2016 | Neural Machine Translation by Jointly Learning to Align and Translate | arXiv | arXiv |
| 2016 | Identity Mappings in Deep Residual Networks | arXiv | arXiv |
| 2017 | Variational Lossy Autoencoder | arXiv | arXiv |
| 2017 | Kolmogorov Complexity and Algorithmic Randomness | Henry Steinitz |
| 2017 | Neural Message Passing for Quantum Chemistry | arXiv | arXiv |
| 2017 | A Simple Neural Network Module for Relational Reasoning | arXiv | arXiv |
| 2017 | Attention Is All You Need | arXiv | arXiv |
| 2018 | The Annotated Transformer | Workshop Paper | nlp.seas.harvard.edu |
| 2018 | Relational Recurrent Neural Networks | arXiv | arXiv |
| 2018 | GPipe: Easy Scaling with Micro-Batch Pipeline Parallelism | arXiv | arXiv |
| 2020 | Scaling Laws for Neural Language Models | arXiv | arXiv |
| 2020 | Dense Passage Retrieval for Open-Domain Question Answering | arXiv | arXiv |
| 2020 | Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks | arXiv | arXiv |
| 2023 | Lost in the Middle: How Language Models Use Long Contexts | arXiv | arXiv |
| 2023 | The Perils & Promises of Fact-checking with Large Language Models | arXiv | arXiv |
| 2023 | Zephyr: Direct Distillation of LM Alignment | arXiv | arXiv |
| 2023 | Better & Faster Large Language Models Via Multi-token Prediction | arXiv | arXiv |
| GitHub |
| 2023 | Direct Preference Optimization (DPO) | GitHub |
| 2024 | The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits | arXiv | N/A |
| 2024 | Mixtral of Experts | N/A |
| 2024 | Learning to Retrieve In‑Context Examples | GitHub |
| 2024 | xLSTM | [GitHub](https://github.co |
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