Transfer Learning for Images Using PyTorch: Essential Training
Learn how to use PyTorch, a popular machine learning framework, to implement transfer learning. This course teaches you how to use PyTorch for a similarly popular technique, transfer learning. Learn how to use PyTorch to implement transfer learning for images, including how to build a fixed feature extractor and freeze neural network layers. Discover how to use learning rates and differential learning rates.
PyTorch quickly became the tool of choice for many deep learning researchers after its initial release in 2017. Jonathan Fernandes demonstrates how to use this popular machine learning framework for a similarly buzzworthy technique: transfer learning, in this course. Jonathan explains the fundamentals of transfer learning, which allows you to use the pretrained parameters of an existing deep-learning model for other tasks, using a hands-on approach. He then demonstrates how to use PyTorch to implement transfer learning for images, including how to create a fixed feature extractor and freeze neural network layers. Discover how to use learning rates and differential learning rates.
The course includes:
- What Is Transfer Learning?
- Transfer Learning: Fixed Feature Extractor
- Fine-Tuning the ConvNet
- Further Techniques
- You can take Transfer Learning for Images Using PyTorch: Essential Training certification course on LinkedIn Learning.
Course rating: N/a
Duration: 1 h 37 m
Certificate: Certificate on completion
Enroll here: linkedin.com/learning/transfer-learning-for-images-using-pytorch-essential-training
