Lecture0-2: CoLaboratory

[이론강의] Lecture.1-2: Artificial Neurons

[구현강의] Lecture.1-3: Affine Functions

[구현강의] Lecture.1-4: Artificial Neurons

[이론강의] Lecture.2-1: Dense Layers

[이론강의] Lecture.2-2: The First Dense Layer

[이론강의] Lecture.2-3: Generalized Dense Layers

[이론강의] Lecture.2-4: Minibatches in Dense Layers

[구현강의] Lecture.2-5: Dense Layers

[구현강의] Lecture.2-6: Cascaded Dense Layers

[구현강의] Lecture.2-7: Model Implementation with Dense Layers

[이론강의] Lecture.3-1: Logit and Sigmoid

[이론강의] Lecture.3-2: Softmax Layer

[구현강의] Lecture.3-3: Binary Classifiers

[구현강의] Lecture.3-4: Multi-class Classifiers

[이론강의] Lecture.4-1: Mean Squared Error

[이론강의] Lecture.4-2: Binary Cross Entropy

[이론강의] Lecture.4-3: Categorical Cross Entropy

[구현강의] Lecture.4-4: Toy Datasets for Regression and Binary Classification

[구현강의] Lecture.4-5: Toy Datasets for Multi-class Classification

[구현강의] Lecture.4-6: MSE and BCE

[구현강의] Lecture.4-7: SCCE and CCE

[이론강의] Lecture.5-1: Image Tensors and Classical Correlation

[이론강의] Lecture.5-2: Computations of Conv Layers

[이론강의] Lecture.5-3: Conv Layers for Multi-channel Input

[구현강의] Lecture.5-4: Conv2D Layers

[구현강의] Lecture.5-5: Conv2D with Filters

[구현강의] Lecture.5-6: Model Implementation with Conv2D Layers

[이론강의] Lecture.6-1: Pooling Layers

[구현강의] Lecture.6-2: Max and Average Pooling Layers

[구현강의] Lecture.6-3: Padding and Strides

[이론강의] Lecture.7-1: Convolutional Neural Networks

[구현강의] Lecture.7-2: Shapes in CNNs

[구현강의] Lecture.7-3: CNN Implementation

[구현강의] Lecture.7-4: LeNet Implementation