TensorFlow 2.0: Working with Images
TensorFlow 2.0: Working with Images
.MP4, AVC, 1280x720, 30 fps | English, AAC, 2 Ch | 40m | 139 MB
Instructor: Jonathan Fernandes
.MP4, AVC, 1280x720, 30 fps | English, AAC, 2 Ch | 40m | 139 MB
Instructor: Jonathan Fernandes
TensorFlow 2.0 is quickly becoming one of the most popular deep learning frameworks and a must-have skill in your artificial intelligence toolkit. Using a hands-on approach, machine learning and AI model expert Jonathan Fernandes shows you the basics of working with images—both grayscale and color—in TensorFlow, and explores transfer learning and other training enhancements such as ModelCheckpoint, EarlyStopping, and TensorBoard.
Learning objectives
- Differentiate between EarlyStopping and ModelCheckpoint callbacks.
- Recognize how a model’s input layer and the associated dataset are related.
- Explain why neural networks do not take spatial structure into account.
- Describe what transfer learning is and why it is so useful.
- Recognize the difference between pretrained models and fine-tuning them.
- Explain the use case for EarlyStopping.
TensorFlow 2.0: Working with Images
