Book Content
chapters • 10h56m total length
1. A Quick Refresher
2. Building our first Neural Network Together
3. Decision Tress and Random Forests
4. Face and Motion Detection
5. Training CNNs using ConvNetSharp
6. Training Autoencoders Using RNNSharp
7. Replacing Back Propagation with PSO
8. Function Optimizations; How and Why
9. Finding Optimal Parameters
10. Object Detection with TensorFlowSharp
11. Time Series Prediction and LSTM Using CNTK
12. GRUs Compared to LSTMs, RNNs, and Feedforward Networks
13. Appendix A- Activation Function Timings
14. Appendix B- Function Optimization Reference
