- Introduction
- Computer Vision Problems
- Linear Layer and Classification Pipeline
- Loss functions and Softmax
- Stochastic Gradient Descend
- PRACTICE #1: Data loading
- PRACTICE #2: Linear Classifier in PyTorch (part 1)
- PRACTICE #3: Linear Classifier in PyTorch (part 2)
- PRACTICE #4: Multi-layer perceptron
- What is image
- Motivation to Convolutions
- Convolution operation
- Parameters of the convolution
- Non-linear function
- Max Pooling and Average Pooling
- Building deep convolutional network
- PRACTICE #5: Convolutional Neural Network
- Overfitting. L2 regularization
- DropOut regularization. DropConnect regularization
- DropBlock regularization
- Early Stopping regularization
- Batch Normalization
- Data Augmentation
- Existing datasets
- Modern Architectures
- Transfer Learning
- Data Loading
- Data Augmentation
- Transfer Learning: ResNet-18