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Ethan Hammar
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Mathematics
  • Foundations
    • Proofs and Logic
    • Discrete Structures
    • Combinatorics
    • Graph Theory
  • Linear Algebra
    • Linear Algebra — Vectors and Matrices
    • Linear Algebra — Vector Spaces
    • Linear Algebra — Eigenvalues and Decompositions
  • Calculus
    • Calculus — Differentiation
    • Calculus — Multivariable Calculus
  • Probability
    • Probability — Foundations
    • Probability — Random Variables and Distributions
    • Probability — Expectation and Concentration
  • Statistics
    • Statistics — Estimation
    • Statistics — Inference and Experimental Design
  • Optimization
    • Optimization — Fundamentals
    • Optimization — Convex Optimization
    • Optimization — Numerical Methods
  • Information and Computation
    • Information Theory
    • Numerical Computation
    • Numerical Linear Algebra
Computer Science
  • Algorithms and Data Structures
    • Data Structures
    • Algorithm Analysis
    • Searching and Sorting
    • Hashing
    • Trees and Heaps
    • Graph Algorithms
    • Greedy Algorithms
    • Dynamic Programming
    • Randomized Algorithms
    • Amortized Analysis
  • Theory of Computation
    • Formal Languages and Automata
    • Computability
    • Complexity Theory
Systems
  • Computer Architecture
    • Data Representation and Machine Instructions
    • Assembly and the Machine Boundary
    • Processor Microarchitecture
    • Pipelines and Branch Prediction
    • Caches and Memory Hierarchy
  • Operating Systems
    • Processes and Threads
    • Operating-System Scheduling
    • Virtual Memory
    • Filesystems and Storage
  • Concurrency
    • Concurrency Fundamentals
    • Synchronization
    • Atomics and Memory Ordering
    • Lock-Free Algorithms
  • Networking
    • Networking Foundations
    • Internet Protocol and Routing
    • Transport Protocols
    • DNS, HTTP and TLS
  • Databases
    • Database Storage Engines
    • Database Indexes
    • Transactions and Isolation
    • Query Planning and Optimization
  • Distributed Systems
    • Distributed-Systems Foundations
    • Replication and Consistency
    • Consensus
    • Distributed Transactions
    • Distributed Storage
  • Compilers
    • Compiler Front Ends
    • Intermediate Representations
    • Compiler Optimization
    • Code Generation and Runtime Systems
  • Performance Engineering
    • Performance Measurement
    • Profiling and Observability
Machine Learning
  • Classical Machine Learning
    • Learning Foundations
    • Linear Regression
    • Logistic Regression
    • Trees and Ensembles
    • Kernel Methods and Support-Vector Machines
    • Clustering
    • Principal Component Analysis
    • Generalization and Model Selection
    • Probabilistic Machine Learning
  • Neural Networks
    • Neural-Network Foundations
    • Backpropagation
    • Automatic Differentiation
    • Deep-Learning Optimization
    • Convolutional Neural Networks
    • Sequence Models
  • Transformers and Language Models
    • Attention
    • Transformers
    • Tokenization
    • Language Modeling
    • Language-Model Training and Adaptation
    • Generative Modeling
  • Reinforcement Learning
    • Reinforcement-Learning Foundations
    • Markov Decision Processes
    • Value Functions and Bellman Equations
    • Temporal-Difference Learning
    • Q-Learning
    • Policy Gradients
    • Actor-Critic Methods
    • Model-Based Reinforcement Learning
    • Exploration
    • Offline Reinforcement Learning
    • Imitation Learning
ML Systems
  • GPU Computing
    • GPU Architecture
    • GPU Execution and Memory
    • CUDA Programming
    • GPU Kernel Optimization
  • Runtimes and Frameworks
    • Tensor Runtimes
    • Automatic-Differentiation Systems
  • ML Compilers
    • ML Compiler Foundations
    • Graph Optimization and Kernel Fusion
  • Training at Scale
    • Distributed Training
    • Training Parallelism
  • Inference
    • Model Serving
    • Inference Runtimes
    • Batching and Scheduling
    • Attention Caching
  • Efficiency
    • Quantization
    • Pruning and Sparsity
    • Knowledge Distillation
    • Small Language Models
    • Edge Machine Learning
    • Hardware-Aware Optimization
    • Algorithm-Hardware Co-Design
Research
  • Methodology
    • Scientific Method for Computer Science
    • Experimental Design
    • Benchmark Methodology
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  1. Wiki
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Machine Learning

  • Classical Machine Learning9 chapters
  • Neural Networks6 chapters
  • Transformers and Language Models6 chapters
  • Reinforcement Learning11 chapters
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