Book Content
chapters • 21h32m total length
1. Neural Network Foundations with TensorFlow 2.0
2. TensorFlow 1.x and 2.x
3. Regression
4. Convolutional Neural Networks
5. Advanced Convolutional Neural Networks
6. Generative Adversarial Networks
7. Word Embeddings
8. Recurrent Neural Networks
9. Autoencoders
10. Unsupervised Learning
11. Reinforcement Learning
12. TensorFlow and Cloud
13. TensorFlow for Mobile and IoT and TensorFlow.js
14. An introduction to AutoML
15. The Math Behind Deep Learning
16. Tensor Processing Unit
