Computer Science Resources
Video lectures on various topics:
MIT 6.875 (Cryptography)
UC Berkeley CS188 Intro to AI
Erlang Master class
Machine Learning 10-701/15-781
Homepages of Computer Science academic courses:
Stanford CS144: Introduction to Computer Networking
MIT 6.S191: Introduction to Deep Learning
CS 525 Advanced Distributed Systems
6.046J Design and Analysis of Algorithms
6.006 Introduction to Algorithms
11-785 Introduction to Deep Learning
MIT OCW 6.035 Computer Language Engineering
CS 421/521: Compilers and Interpreters
CS 6120: Advanced Compilers: The Self-Guided Online Course
Foundations of Probabilistic Programming
Advanced Functional Programming
CS 4110: Programming Languages and Logics
6.851 Advanced Data Structures
6.004 Computation Structures
6.080 Great Ideas in Theoretical Computer Science
6.006 Introduction to Algorithms
6.893 Philosophy and Theoretical Computer Science
Machine Learning
An Introduction to Computer Networks
cs4414: Operating Systems
Computer Science pedagogical:
Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory https://arxiv.org/abs/2310.20360
Information Theory and Statistical Physics - Lecture Noteshttps://arxiv.org/abs/1006.1565
Information Theory: A Tutorial Introduction https://arxiv.org/abs/1802.05968
A Brief Introduction to Machine Learning for Engineers https://arxiv.org/abs/1709.02840
Pen and Paper Exercises in Machine Learning https://arxiv.org/abs/2206.13446
Formal Algorithms for Transformers https://arxiv.org/abs/2207.09238
A Cookbook of Self-Supervised Learning https://arxiv.org/abs/2304.12210
Information Theory: A Tutorial Introduction https://arxiv.org/abs/1802.05968
Tutorial on Diffusion Models for Imaging and Vision https://arxiv.org/abs/2403.18103
Computer Science papers (arxiv):
Explainable Deep Learning: A Field Guide for the Uninitiated https://arxiv.org/abs/2004.14545
Dreaming neural networks: forgetting spurious memories and reinforcing pure ones https://arxiv.org/abs/1810.12217
Learning second order coupled differential equations that are subject to non-conservative forces https://arxiv.org/abs/2010.11270
Memristor - The fictional circuit element https://arxiv.org/abs/1808.05982
Thermodynamics of stochastic Turing machines https://arxiv.org/abs/1506.00894
How to Run Algorithmic Information Theory on a Computer https://arxiv.org/abs/chao-dyn/9509014v2
Let a Thousand Flowers Bloom: An Algebraic Representation for Edge Graphs https://arxiv.org/abs/2403.02273
Understanding Biology in the Age of Artificial Intelligence https://arxiv.org/abs/2403.04106
Toward Next-Generation Artificial Intelligence: Catalyzing the NeuroAI Revolution https://arxiv.org/abs/2210.08340
Perfect Zero-Knowledge PCPs for #P https://arxiv.org/abs/2403.11941
Deep Probabilistic Programming https://arxiv.org/abs/1701.03757v2
Move Evaluation in Go Using Deep Convolutional Neural Networks http://arxiv.org/abs/1412.6564
Teaching Deep Convolutional Neural Networks to Play Go https://arxiv.org/abs/1412.3409
A Probabilistic Theory of Deep Learning http://arxiv.org/abs/1504.00641
Ask Me Anything: Dynamic Memory Networks for Natural Language Processing http://arxiv.org/abs/1506.07285
A New Information Complexity Measure for Multi-pass Streaming with Applications https://arxiv.org/abs/2403.20283v1
Machine Culture https://arxiv.org/abs/2311.11388
Attention Is All You Need https://arxiv.org/abs/1706.03762
Eight Transaction Papers by Jim Gray https://arxiv.org/abs/2310.04601
What's the Magic Word? A Control Theory of LLM Prompting https://arxiv.org/abs/2310.04444
Other papers / links:
The Original 'Lambda Papers' by Guy Steele and Gerald Sussman
Category Theory for Programmers