Expediting Deep Learning with Transfer Learning: PyTorch Playbook

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This course covers the important design choices that a data professional must make while leveraging pre-trained models using Transfer Learning. It also covers the implementation aspects of different Transfer Learning approaches in PyTorch.

1. Course Overview

1. Course Overview

2. Getting Started with Transfer Learning

01. Version Check

02. Module Overview

03. Prerequisites and Course Outline

04. Introducing Transfer Learning

05. Source and Target Domains, Source and Target Tasks

06. Categorizing Transfer Learning

07. Transfer Learning Scenarios

08. Freeze or Fine-tune Layers

09. Benefits of Transfer Learning

10. Pre-trained Models in PyTorch

11. Setting up a Deep Learning Virtual Machine on the Google Cloud Platfo

12. Exploring Pre-trained Models in PyTorch

13. Module Summary

3. Performing Feature Extraction Using Transfer Learning

1. Module Overview

2. Uploading Datasets to Use for Image Classification

3. Exploring and Loading the Cats and Dogs Dataset

4. Using the VGG16 Model as a Fixed Feature Extractor

5. Training the Final Layer and Using the Model for Predict

6. Exploring and Loading the Oil Spill Dataset

7. Freezing Lower Layers and Fine-tuning Weights of Top Lay

8. Fine-tuning Top Layers

9. Module Summary

4. Reusing Model Architectures and Designs

1. Module Overview

2. Exploring and Loading the Chest X-Ray Dataset

3. Training a Model from Scratch

4. Exploring and Loading the Natural Images Dataset

5. Fine-tuning the Network

6. Cleaning up Resources

7. Summary and Further Study

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تولید کننده: PluralSight