Questions

1. Why should we convert integer inputs into float values during training?
2. What are the various methods to reshape a tensor object?
3. Why is computation faster with tensor objects over NumPy arrays?
4. What constitutes the init magic function in a neural network class?
5. Why do we perform zero gradients before performing back-propagation?
6. What magic functions constitute the dataset class?
7. How do we make predictions on new data points?
8. How do we fetch the intermediate layer values of a neural network?
9. How does the sequential method help in simplifying defining the architecture of a neural network?