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1. Project
CS229
CS229
CS229 9. Approx, Estimator Error & ERM
CS229 10. Decision Trees & Ensemble Methods
CS229 11. Introduction to Neural Networks
CS229 12. Backprop & Improving Neural Network
CS229 13. Debugging ML Models & Error Analysis
CS229 14. Expectation-Maximization Algorithms
CS231n
CS231n 1. Image Classification
CS231n 2-1. Linear Classification (Score Function)
CS231n 2-2. Linear Classification (Loss function)
CS231n 2-3 Linear Classification(Regularization)
CS231n 4-1. Backpropagation (gradient for neural network)
CS231n 4-2. Setting Up Architecture (Neural Network)
CS231n 5. Convolutional Neural Network (CNN)
CS231n 6-1. Setting Up Architecture (Activation Functions)
CS231n 6-2. Setting Up the Data and the Loss
Cs231n 7-1. Optimization
CS231n 7-2. Evaluation & Regularization
CS231n 9. CNN Architectures
CS231n 10-1. Recurrent Neural Networks(Single RNN)
CS231n 10-2. Multilayer RNN
CS231n 11. Detection & Segmentation
CS231n 12. Visualizing and Understanding
CS231n 13. Generative Models
CS231n 14. Deep Reinforcement Learning
논문-RL Trading
Review - Deep Reinforcement Learning in Quantitative Algorithmic Trading
Review - Reinforcement Learning for Quantitative Learning
강화학습 기반 주식 자동 매매모델 전략 제안(824)
딥러닝기반 강화학습 모델 서능비교(381)
2. Area
논문-RL
DDQN(2015) Deep Reinforcement Learning with Double Q-learning
DQN(2015) Human-level control through deep reinforcement learning
논문-Visual
ResNet
4. Archive
Linear Algebra 이해하기
Linear Algebra 2. Linear combination, span and basis vectors
Linear Algebra 3. Linear transformation and matrices
Linear Algebra 4. Matrix multiplication
Linear Algebra 6. Determinant
다이어리
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Tag: PDF
Tag: PDF
1 item with this tag.
Dec 22, 2024
CS231n 13. Generative Models
autoencoder
PDF
likelihood
Bayes_Rule
