- 작은(Little) 모바일 기기의 화면에서 읽기 좋게 편집된 François Fleure 교수의 딥러닝 기초 서적
I. Foundations
- Machine Learning
1.1 Learning from data
1.2 Basis function regression
1.3 Under and over-fitting
1.4 Categories of models
- Efficient Computation
2.1 GPUs, TPUs, and batches
2.2 Tensors
- Training
3.1 Losses
3.2 Autoregressive models
3.3 Gradient descent
3.4 Backpropagation
3.5 Training protocols
3.6 Training data
II. Deep Models
- Model Components
4.1 The notion of layer
4.2 Linear layers
4.3 Activation functions
4.4 Pooling
4.5 Dropout
4.6 Normalizing layers
4.7 Skip connections
4.8 Attention layers
4.9 Token embedding
4.10 Positional encoding
- Architectures
5.1 Multi-Layer Perceptrons
5.2 Convolutional networks
5.3 Attention models
III. Applications
- Prediction
6.1 Image denoising
6.2 Image classification
6.3 Object detection
6.4 Semantic segmentation
6.5 Speech recognition
6.6 Text-image representations
- Synthesis
7.1 Text generation
7.2 Image generation