~/ml/dl_theory

2024

05-16 Attention
05-15 Seq2Seq
05-14 RNN Back Propagation
05-14 RNN
05-13 CNN Summary
05-12 VGGNet
05-11 CNN Feature Map
05-10 Padding, Pooling, Striding
05-09 CNN with 3D input
05-09 CNN Feature Extraction
05-08 CNN
05-07 Regularization
05-06 Dropout
05-06 Overfitting & Data Augmentation
05-05 Loss Landscape / Skip-Connection
05-03 Batch Normalization
05-02 Vanishing Gradient
05-02 ReLU
05-01 Universal Approximation Theorem
04-30 Multi-class Classification
04-29 Neural Network is MLE machine
04-28 MSE vs. Likelihood
04-27 Perceptron
04-27 Binary Classification (Sigmoid)
04-26 Back Propagation with Matrix Derivatives
04-25 Back Propagation
04-24 MLP & Non-Linearity
04-23 K-fold Cross Validation
04-23 Train, Valid, Test
04-22 Adam
04-22 Momentum vs. RMSProp
04-21 Mini-Batch SGD
04-21 Stochastic Gradient Descent
04-20 Weight Initialization
04-20 Gradient Descent
04-19 Linear Regression
04-19 Neural Network