Deep-Learning-with-PyTorch

Summary
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• We will use PyTorch’s torch.optim.SGD (stochastic gradient descent) optimizer
with a learning rate of 0.001 and a momentum of 0.99 for the majority of part 2.
These values are also reasonable defaults for many deep learning projects.
• Our initial model for classification will be very similar to the model we used in
chapter 8. This lets us get started with a model that we have reason to believe
will be effective. We can revisit the model design if we think it’s the thing preventing
our project from performing better.
• The choice of metrics that we monitor during training is important. It is easy to
accidentally pick metrics that are misleading about how the model is performing.
Using the overall percentage of samples classified correctly is not useful for
our data. Chapter 12 will detail how to evaluate and choose better metrics.
• TensorBoard can be used to display a wide range of metrics visually. This makes
it much easier to consume certain forms of information (particularly trend
data) as they change per epoch of training.