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The six lines of code we saw in are just one small part of the process of using deep learning in practice. In this chapter, we're going to use a computer vision example to look at the end-to-end process of creating a deep learning application. More specifically, we're going to build a bear classifier! In the process, we'll discuss the capabilities and constraints of deep learning, explore how to create datasets, look at possible gotchas when using deep learning in practice, and more. Many of the key points will apply equally well to other deep learning problems, such as those in . If you work through a problem similar in key respects to our example problems, we expect you to get excellent results with little code, quickly. Let's start with how you should frame your problem. We've seen that deep learning can solve a lot of challenging problems quickly and with little code. As a beginner, there's a sweet spot of problems that are similar enough to our example problems that you can v

Jupyter Notebook Viewer fastbook 02_production.ipynb Notebook In [ ]: #hide ! [ -e /content ] && pip install -Uqq fastbook import fastbook fastbook . setup_book () In [ ]: #hide from fastbook import * from fastai.vision.widgets import * [[chapter_production]] From Model to Production ¶ The six lines of code we saw in <<chapter_intro>> are just one small part of the process of using deep learning in practice. In this chapter, we're going to use a computer vision example to look at the end-to-end process of creating a deep learning application. More specifically, we're going to build a bear clas

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