Foundations of Deep Learning: Wrap up

Congratulations, you have made it to the end of the “foundations of deep learning” section of the book! You now understand how all of fastai’s applications and most important architectures are built, and the recommended ways to train them—and you have all the information you need to build these from scratch. While you probably won’t need to create your own training loop, or batchnorm layer, for instance, knowing what is going on behind the scenes is very helpful for debugging, profiling, and deploying your solutions.

Since you understand the foundations of fastai’s applications now, be sure to spend some time digging through the source notebooks and running and experimenting with parts of them. This will give you a better idea of how everything in fastai is developed.

In the next section, we will be looking even further under the covers: we’ll explore how the actual forward and backward passes of a neural network are done, and we will see what tools are at our disposal to get better performance. We will then continue with a project that brings together all the material in the book, which we will use to build a tool for interpreting convolutional neural networks. Last but not least, we’ll finish by building fastai’s Learner class from scratch.

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