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MIT18.01/18.02: Calculus
MIT18.06: Linear Algebra
MIT6.050J: Information theory and Entropy
📉 数学进阶
📉 数学进阶
UCB CS70: discrete Math and probability theory
UCB CS126: probability theory
MIT 6.042J: Mathematics for Computer Science
MIT18.330: Introduction to numerical analysis
Standford EE364A: Convex Optimization
The Information Theory, Pattern Recognition, and Neural Networks
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📏 编程入门
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📐 数据结构与算法
UCB CS61B: Data Structures and Algorithms
Coursera: Algorithms I & II
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MIT 6.046: Design and Analysis of Algorithms
UCB CS170: Efficient Algorithms and Intractable Problems
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🎚️ 软件工程
MIT 6.031: Software Construction
UCB CS169: software engineering
CMU 17-803: Empirical Methods
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🗞️ 计算机系统基础
CMU 15-213: CSAPP
Stanford CS110: Principles of Computer Systems
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Coursera: Nand2Tetris
UCB CS61C: Great Ideas in Computer Architecture
ETHz: Digital Design and Computer Architecture
ETHz: Computer Architecture
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UCB CS162: Operating System
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HIT OS: Operating System
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🗄️ 并行与分布式系统
CMU 15-418/Stanford CS149: Parallel Computing
MIT 6.824: Distributed System
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UCB CS161: Computer Security
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ASU CSE365: Introduction to Cybersecurity
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Cambridge: Semantics of Programming Languages
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UCB Data100: Principles and Techniques of Data Science
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Harvard CS50's Introduction to AI with Python
UCB CS188: Introduction to Artificial Intelligence
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🖇️ 机器学习
Coursera: Machine Learning
Stanford CS229: Machine Learning
UCB CS189: Introduction to Machine Learning
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📎 机器学习系统
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CMU 10-414/714: Deep Learning Systems
Machine Learning Compilation
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📋 深度学习
UMich EECS 498-007 / 598-005: Deep Learning for Computer Vision
Coursera: Deep Learning
国立台湾大学: 李宏毅机器学习
Stanford CS231n: CNN for Visual Recognition
Stanford CS224n: Natural Language Processing
Stanford CS224w: Machine Learning with Graphs
UCB CS285: Deep Reinforcement Learning
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🗡️ 机器学习进阶
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CMU 10-708: Probabilistic Graphical Models
Columbia STAT 8201: Deep Generative Models
U Toronto STA 4273 Winter 2021: Minimizing Expectations
Stanford STATS214 / CS229M: Machine Learning Theory
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