AI Basic Implementations
- Basics
- 1-1. Basics of Python
- 1-2. Basics of Numpy
- 1-3. Introduction to PyTorch
- Multi-Layer Perceptron (MLP) and Model Selection
- 2-1. MLP
- 2-2. k-fold cross validation
- CNNs
- 3-1. Simple CNN (basic)
- 3-2. ResNet (with torchvision)
- 3-3. Simple CNN (Scratch)
- 3-4. Simple CNN with k-fold cross validation (Scratch)
- 3-5. MobileNet, EfficeintNet (Scratch)
- GNNs
- 4-1. GNN - Graph Classification Task
- 4-2. GNN - Node Classification Task
- RNNs
- 5-1. Simple RNN (Basic)
- 5-2. LSTM
- 5-3. LSTM, Seq2Seq (Scratch)
- 5-4. Text Classification Model with RNN, GRU
- Generative Models
- 6-1. Variational Autoencoder (VAE)
- 6-2. Generative Adversarial Network (GAN)
- Transformers
- 7-1. Transformer (Scratch)
- 7-2. Transformer (hugging-face)
- 7-3. BERT fine-tuning
- Distributed Learning (Ray, DP, DDP)
- 8-1. Distributed Learning
- 8-2. Ray