Questions

1. We went through a lot in this chapter. You can use the framework provided to investigate the properties of neural networks. Try several activation functions, or different settings for convolutions, to see what changes in the training process.
2. Draw a diagram of an artificial neuron and label the parts. Look up a natural, human biological neuron and compare them.
3. Which features of a real neuron and an artificial neuron are the same? Which ones are different?
4. What effect does the learning rate have on gradient descent? What if the learning rate is too large? Too small?
5. What relationship does the first layer of a neural network have with the input?
6. What relationship does the last layer of a neural network have with the output?
7. Look up three kinds of loss functions and describe how they work. Include mean square loss and the two kinds of cross-entropy loss.
8. What would you change if your network was trained and reached 40% accuracy of the classification...