Daniel Voigt Godoy - Deep Learning with PyTorch Step-by-Step A Beginner’s Guide-leanpub

Recap
First of all, congratulations are in order: You have successfully implemented a fully
functioning model and training loop in PyTorch!
We have covered a lot of ground in this first chapter:
• implementing a linear regression in Numpy using gradient descent
• creating tensors in PyTorch, sending them to a device, and making parameters
out of them
• understanding PyTorch’s main feature, autograd, to perform automatic
differentiation using its associated properties and methods, like backward(),
grad, zero_(), and no_grad()
• visualizing the dynamic computation graph associated with a sequence of
operations
• creating an optimizer to simultaneously update multiple parameters, using its
step() and zero_grad() methods
• creating a loss function using PyTorch’s corresponding higher-order function
(more on that topic in the next chapter)
• understanding PyTorch’s Module class and creating your own models,
implementing __init__() and forward() methods, and making use of its builtin
parameters() and state_dict() methods
• transforming the original Numpy implementation into a PyTorch one using the
elements above
• realizing the importance of including model.train() inside the training loop
(never forget that!)
• implementing nested and sequential models using PyTorch’s layers
• putting it all together into neatly organized code divided into three distinct
parts: data preparation, model configuration, and model training
You are now ready for the next chapter. We’ll see more of PyTorch’s capabilities,
and we’ll further develop our training loop so it can be used for different problems
and models. You’ll be building your own, small draft of a library for training deep
learning models.
[39] https://github.com/dvgodoy/PyTorchStepByStep/blob/master/Chapter01.ipynb
[40] https://colab.research.google.com/github/dvgodoy/PyTorchStepByStep/blob/master/Chapter01.ipynb
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