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The key outcome of lesson 1 is that we’ll have trained an image classifier which can recognize pet breeds at state of the art accuracy. The key to this success is the use of *transfer learning*, which will be a key platform for much of this course. We’ll also see how to analyze the model to understand its failure modes. In this case, we’ll see that the places where the model is making mistakes is in the same areas that even breeding experts can make mistakes.
We’ll discuss the overall approach of the course, which is somewhat unusual in being *top-down* rather than *bottom-up*. So rather than starting with theory, and only getting to practical applications later, instead we start with practical applications, and then gradually dig deeper and deeper in to them, learning the theory as needed. This approach takes more work for teachers to develop, but it’s been shown to help students a lot, for example in education research at Harvard (https://www.gse.harvard.edu/news/uk/0…) by David Perkins.
We also discuss how to set the most important *hyper-parameter* when training neural networks: the *learning rate*, using Leslie Smith’s fantastic *learning rate finder* method. Finally, we’ll look at the important but rarely discussed topic of *labeling*, and learn about some of the features that fastai provides for allowing you to easily add labels to your images.
Note that to follow along with the lessons, you’ll need to connect to a cloud GPU provider which has the fastai library installed (recommended; it should take only 5 minutes or so, and cost under $0.50/hour), or set up a computer with a suitable GPU yourself (which can take days to get working if you’re not familiar with the process, so we don’t recommend it). You’ll also need to be familiar with the basics of the *Jupyter Notebook* environment we use for running deep learning experiments. Up to date tutorials and recommendations for these are available from the course website (http://course.fast.ai).