Chapter 2 - PyTorch Fundamentals

1. Why should we convert integer inputs into float values during training?
   nn.Linear (and almost all torch layers) only accepts floats as inputs
2. What are the various methods to reshape a tensor object?
   reshape, view
3. Why is computation faster with tensor objects over NumPy arrays?
   Capability to run on GPUs in parallel is only available on tensor objects
4. What constitutes the init magic function in a neural network class?
   Calling super().__init__() and specifying the neural network layers
5. Why do we perform zero gradients before performing back-propagation?
   To ensure gradients from previous calculations are flushed out
6. What magic functions constitute the dataset class?
   __len__ and __getitem__
7. How do we make predictions on new data points?
   By calling the model on the tensor as if it is a function – model(x)
8. How do we fetch the intermediate layer values of a neural network?
   By creating a custom method
9. How does the Sequential method help in simplifying defining...