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

the filters learned by the model produce the features that will feed the
classifier part
• computing accuracy for a multiclass classification problem
• creating a static method to apply a function to all the mini-batches in a data
loader
Congratulations: You took one big step toward being able to tackle many
computer vision problems. This chapter introduced the fundamental concepts
related to (almost) all things convolutional. We still need to add some more tricks to
our arsenal, so we can make our models even more powerful. In the next chapter,
we’ll learn about convolutions over multiple channels, using dropout layers to
regularize a model, finding a learning rate, and the inner workings of optimizers.
[88] https://github.com/dvgodoy/PyTorchStepByStep/blob/master/Chapter05.ipynb
[89] https://colab.research.google.com/github/dvgodoy/PyTorchStepByStep/blob/master/Chapter05.ipynb
[90] https://en.wikipedia.org/wiki/Convolution
[91] https://en.wikipedia.org/wiki/Kernel_(image_processing)
[92] https://bit.ly/3sJ7Nn7
[93] https://realpython.com/primer-on-python-decorators/
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