- Preface
- 1. Introduction to TensorFlow
- 2. Introduction to Computer Vision
- 3. Going Beyond the Basics: Detecting Features in Images
- 4. Using Public Datasets with TensorFlow Datasets
- 5. Introduction to Natural Language Processing
- 6. Making Sentiment Programmable Using Embeddings
- 7. Recurrent Neural Networks for Natural Language Processing
- 8. Using TensorFlow to Create Text
- 9. Understanding Sequence and Time Series Data
- 10. Creating ML Models to Predict Sequences
- 11. Using Convolutional and Recurrent Methods for Sequence Models
- 12. An Introduction to TensorFlow Lite
- 13. Using TensorFlow Lite in Android Apps
- 14. Using TensorFlow Lite in iOS Apps
- 15. An Introduction to TensorFlow.js
- 16. Coding Techniques for Computer Vision in TensorFlow.js
- 17. Reusing and Converting Python Models to JavaScript
- 18. Transfer Learning in JavaScript
- 19. Deployment with TensorFlow Serving
- 20. AI Ethics, Fairness, and Privacy
- Fairness in Programming
- Fairness in Machine Learning
- Tools for Fairness
- Federated Learning
- Step 1. Identify Available Devices for Training
- Step 2. Identify Suitable Available Devices for Training
- Step 3. Deploy a Trainable Model to Your Training Set
- Step 4. Return the Results of the Training to the Server
- Step 5. Deploy the New Master Model to the Clients
- Secure Aggregation with Federated Learning
- Federated Learning with TensorFlow Federated
- Google’s AI Principles
- Summary
- Index
AI and Machine Learning for Coders
A Programmer’s Guide to Artificial Intelligence
Laurence Moroney
-
No Comment
..................Content has been hidden....................
You can't read the all page of ebook, please click here login for view all page.