A Beginner's Guide to Artificial Neural Networks in Python with Keras and TensorFlow
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1. How to get started
2. Building the network (07:10)
3. Compile the network (09:09)
4. The initial weights (10:15)
5. Training the network (11:20)
6. Extract the weights (12:54)
7. Compare with logistic regression (13:16)
8. Use the trained network to predict (13:40)
9. The loss function (15:20)
10. Compute the accuracy (17:08)
11. Confusion matrix (18:25)
12. Plot an ROC curve (19:28)
13. Plot how the network predicts (20:35)
14. Add hidden layers (21:35)
15. Changing the seed (25:12)
16. Plot the loss (26:35)
17. The learning rate (28:05)
18. The batch size (30:01)
19. Save and load the network (33:01)
20. Retraining a saved network (34:45)
21. Modifying an existing network (37:16)
22. The MobileNet (39:44)
23. Sequential API vs Functional API (41:35)
24. Normalize the data (44:08)
25. How to use more than one input feature/node (48:19)
26. A multiclass classification network and the SoftMax function (49:50)
27. Sparse categorical cross-entropy vs categorical cross-entropy loss function and one-hot encoding (57:24)
28. Hyperparameters (01:00:18)
29. Cross-validation (01:02:15)
30. A network with a continuous output (01:09:20)
31. Early stopping (01:14:50)
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