Day1 : Deep Learning & Keras 기본 이해 및 실습
- Deep Learning 이론/구성 요소
- 딥러닝 프레임워크 개요
- Keras기본 구조 및 실습
Day2 : Artificial Neural Networks
- Intro to ANN (Artificial Neural Networks)
- Perceptron & Backpropagation
- Vanishing Gradient Problem
- Activation Functions
- Optimization
- Loss function
Day3 : CNN / RNN
- CNN (Convolutional Neural Networks) 개념 / 특징 / 구조
- Convolutional layers, Activation, Pooling, Batch Normalization, Dropout
- RNN (Recurrent Neural Network) 개념 / 특징 / 구조
- LSTM Architecture 개념
- Deep Learning 이론/구성 요소
- 딥러닝 프레임워크 개요
- Keras기본 구조 및 실습
Day2 : Artificial Neural Networks
- Intro to ANN (Artificial Neural Networks)
- Perceptron & Backpropagation
- Vanishing Gradient Problem
- Activation Functions
- Optimization
- Loss function
Day3 : CNN / RNN
- CNN (Convolutional Neural Networks) 개념 / 특징 / 구조
- Convolutional layers, Activation, Pooling, Batch Normalization, Dropout
- RNN (Recurrent Neural Network) 개념 / 특징 / 구조
- LSTM Architecture 개념
