Discover how to implement transfer learning using PyTorch, the popular machine learning framework.
Transfer Learning for Images Using PyTorch: Essential Training
Overview
Syllabus
Introduction
- Welcome
- What you should know before watching this course
- What is transfer learning?
- VGG16
- CIFAR-10 dataset
- Creating a fixed feature extractor
- Understanding loss: CrossEntropyLoss() and NLLLoss()
- Autograd
- Using autograd
- Training the fixed feature extractor
- Optimizers
- CPU to GPU
- Train the extractor
- Evaluate the network and viewing images
- Viewing images and normalization
- Accuracy of the model
- Fine-tuning
- Using fine-tuning
- Training from the fully connected network onwards
- Unfreezing and training over the last CNN block onwards
- Unfreezing and training over the last two CNN block onwards
- Learning rates
- Differential learning rates
- Next steps
Taught by
Jonathan Fernandes
