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In this course, you will learn:
- PyTorch quickly became the tool of choice for many deep learning researchers. In this course, Jonathan Fernandes shows you how to leverage this popular machine learning framework for a similarly buzzworthy technique: transfer learning.
- Learn how to implement transfer learning for images using PyTorch, including how to create a fixed feature extractor and freeze neural network layers. Plus, find out about using learning rates and differential learning rates.
Syllabus:
- Introduction
- What you should know before watching this course
1. What Is Transfer Learning?
- What is transfer learning?
- VGG16
- CIFAR-10 dataset
2. Transfer Learning: Fixed Feature Extractor
- 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
3. Fine-Tuning the ConvNet
- Fine-tuning
- Using fine-tuning
- Unfreezing and training over the last CNN block onwards
- Unfreezing and training over the last two CNN block onwards
4. Further Techniques
- Learning rates
- Differential learning rates
