Book Content
chapters • 15h48m total length
1. Tensorflow 101
2. High-Level Libraries for TensorFlow
3. Keras 101
4. Classical Machine Learning with TensorFlow
5. Neural Networks and MLP with TensorFlow and Keras
6. RNN with TensorFlow and Keras
7. RNN for Time Series Data with TensorFlow and Keras
8. RNN for Text Data with TensorFlow and Keras
9. CNN with TensorFlow and Keras
10. Autoencoder with TensorFlow and Keras
11. TensorFlow Models in Production with TF Serving
12. Transfer Learning and Pre-Trained Models
13. Deep Reinforcement Learning
14. Generative Adversarial Networks
15. Distributed Models with TensorFlow Clusters
16. TensorFlow Models on Mobile and Embedded Platforms
17. TensorFlow and Keras in R
18. Debugging TensorFlow Models
19. TensorFlow Processing Units
