7 Teaching machines to see better: Improving CNNs and making them confess
- Reducing overfitting of image classifiers
- Boosting model performance via better model architectures
- Image classification using pretrained models and transfer learning
- Modern ML explainability techniques to dissect image classifiers
7.1 Techniques for reducing overfitting
7.1.1 Image data augmentation with Keras
7.1.2 Dropout: Randomly switching off parts of your network to improve generalizability
7.1.3 Early stopping: Halting the training process if the network starts to underperform
7.2 Toward minimalism: Minception instead of Inception
7.2.1 Implementing the stem
7.2.2 Implementing Inception-ResNet type A block
7.2.3 Implementing the Inception-ResNet type B block
7.2.4 Implementing the reduction block
7.2.5 Putting everything together
7.2.6 Training Minception
7.3 If you can't beat them, join ‘em: Using pretrained networks for enhancing performance
7.3.1 Transfer learning: Reusing existing knowledge in deep neural networks
7.4 Grad-CAM: Making CNNs confess