Deep Learning in Practice: That’s a Wrap!

Congratulations! You’ve made it to the end of the first section of the book. In this section we’ve tried to show you what deep learning can do, and how you can use it to create real applications and products. At this point, you will get a lot more out of the book if you spend some time trying out what you’ve learned. Perhaps you have already been doing this as you go along—in which case, great! If not, that’s no problem either… Now is a great time to start experimenting yourself.

If you haven’t been to the book’s website yet, head over there now. It’s really important that you get yourself set up to run the notebooks. Becoming an effective deep learning practitioner is all about practice, so you need to be training models. So, please go get the notebooks running now if you haven’t already! And also have a look on the website for any important updates or notices; deep learning changes fast, and we can’t change the words that are printed in this book, so the website is where you need to look to ensure you have the most up-to-date information.

Make sure that you have completed the following steps:

• Connect to one of the GPU Jupyter servers recommended on the book’s website.
• Run the first notebook yourself.
• Upload an image that you find in the first notebook; then try a few different images of different kinds to see what happens.
• Run the second notebook, collecting your own dataset based on image search queries that you come up with.
• Think about how you can use deep learning to help you with your own projects, including what kinds of data you could use, what kinds of problems may come up, and how you might be able to mitigate these issues in practice.

In the next section of the book you will learn about how and why deep learning works, instead of just seeing how you can use it in practice. Understanding the how and why is important for both practitioners and researchers, because in this fairly new field nearly every project requires some level of customization and debugging. The better you understand the foundations of deep learning, the better your models will be. These foundations are less important for executives, product managers, and so forth (although still useful, so feel free to keep reading!), but they are critical for anybody who is actually training and deploying models themselves.

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