Shusen Wang

RL-1G Summary

Shusen Wang

RL-1F Evaluate Reinforcement Learning

Shusen Wang

RL-1E Value Functions

Shusen Wang

RL-1D Rewards and Returns

Shusen Wang

RL-1C Randomness in MDP, Agent-Environment Interaction

Shusen Wang

RL-1B State, Action, Reward, Policy, State Transition

Shusen Wang

RL-1A Random Variables, Observations, Random Sampling

Shusen Wang

Vision Transformer for Image Classification

Shusen Wang

BERT for pretraining Transformers

Shusen Wang

Transformer Model (22) Build a Deep Neural Network (1.25x speed recommended)

Shusen Wang

Transformer Model (12) Attention Layers

Shusen Wang

Self-Attenion for RNN (1.25x speed recommended)

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Attention for RNN Seq2Seq Models (1.25x speed recommended)

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Few-Shot Learning (33) Pretraining + Fine-tuning

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Few-Shot Learning (23) Siamese Networks

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17-4 Random Shuffle & Fisher-Yates Algorithm

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5-2 Dense Matrices row-major order, column-major order

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5-1 Matrix basics additions, multiplications, time complexity analysis

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17-1 Monte Carlo Algorithms

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3-1 Insertion Sort

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6-1 Binary Tree Basics

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Few-Shot Learning (13) Basic Concepts

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2-3 Skip List

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2-2 Binary Search

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2-1 Array, Vector, and List Comparisons

Shusen Wang