Book Content
chapters • 15h total length
1. Data science: Bird's-eye view
2. Data Modeling in Action - The Titanic Example
3. Feature Engineering and Model Complexity – The Titanic Example Revisited
4. Get Up and Running with TensorFlow
5. Tensorflow in Action - Some Basic Examples
6. Deep Feed-forward Neural Networks - Implementing Digit Classification
7. Introduction to Convolutional Neural Networks
8. Object Detection – CIFAR-10 Example
9. Object Detection – Transfer Learning with CNNs
10. Recurrent-Type Neural Networks - Language modeling
11. Representation Learning - Implementing Word Embeddings
12. Neural sentiment Analysis
13. Autoencoders – Feature Extraction and Denoising
14. Generative Adversarial Networks in Action - Generating New Images
15. Face Generation and Handling Missing Labels
16. Appendix - Implementing Fish Recognition
