English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 40m 16s | 366 MB
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.
Table of Contents
Introduction
1 Work with gray and color images using transfer learning and fine-tuning
2 What you should know
3 What is TensorFlow
Neural Networks and Images
4 Review of neural networks
5 Working with color images and neural networks
6 Challenge Experiment with hyperparameters
7 Solution Experiment with hyperparameters
Transfer Learning
8 Why the poor performance with neural networks
9 TensorFlow Hub
10 What is transfer learning
11 Transfer learning with TensorFlow Hub
12 TensorFlow Hub for CIFAR-10
Monitoring the Training Process
13 Monitoring the training process
14 Using ModelCheckpoint
15 Working with EarlyStopping
16 Using TensorBoard
Conclusion
17 Next steps
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