How to Learn Deep Learning

Harvard professor David Perkins, who wrote Making Learning Whole (Jossey-Bass), has much to say about teaching. The basic idea is to teach the whole game. That means that if you’re teaching baseball, you first take people to a baseball game or get them to play it. You don’t teach them how to wind twine to make a baseball from scratch, the physics of a parabola, or the coefficient of friction of a ball on a bat.

Paul Lockhart, a Columbia math PhD, former Brown professor, and K-12 math teacher, imagines in the influential essay “A Mathematician’s Lament” a nightmare world where music and art are taught the way math is taught. Children are not allowed to listen to or play music until they have spent over a decade mastering music notation and theory, spending classes transposing sheet music into a different key. In art class, students study colors and applicators, but aren’t allowed to actually paint until college. Sound absurd? This is how math is taught–-we require students to spend years doing rote memorization and learning dry, disconnected fundamentals that we claim will pay off later, long after most of them quit the subject.

Unfortunately, this is where many teaching resources on deep learning begin–-asking learners to follow along with the definition of the Hessian and theorems for the Taylor approximation of your loss functions, without ever giving examples of actual working code. We’re not knocking calculus. We love calculus, and Sylvain has even taught it at the college level, but we don’t think it’s the best place to start when learning deep learning!

In deep learning, it really helps if you have the motivation to fix your model to get it to do better. That’s when you start learning the relevant theory. But you need to have the model in the first place. We teach almost everything through real examples. As we build out those examples, we go deeper and deeper, and we’ll show you how to make your projects better and better. This means that you’ll be gradually learning all the theoretical foundations you need, in context, in such a way that you’ll see why it matters and how it works.

So, here’s our commitment to you. Throughout this book, we will follow these principles:

• Teaching the whole game. We’ll start by showing how to use a complete, working, very usable, state-of-the-art deep learning network to solve real-world problems, using simple, expressive tools. And then we’ll gradually dig deeper and deeper into understanding how those tools are made, and how the tools that make those tools are made, and so on…
• Always teaching through examples. We’ll ensure that there is a context and a purpose that you can understand intuitively, rather than starting with algebraic symbol manipulation.
• Simplifying as much as possible. We’ve spent years building tools and teaching methods that make previously complex topics very simple.
• Removing barriers. Deep learning has, until now, been a very exclusive game. We’re breaking it open, and ensuring that everyone can play.

The hardest part of deep learning is artisanal: how do you know if you’ve got enough data, whether it is in the right format, if your model is training properly, and, if it’s not, what you should do about it? That is why we believe in learning by doing. As with basic data science skills, with deep learning you only get better through practical experience. Trying to spend too much time on the theory can be counterproductive. The key is to just code and try to solve problems: the theory can come later, when you have context and motivation.

There will be times when the journey will feel hard. Times where you feel stuck. Don’t give up! Rewind through the book to find the last bit where you definitely weren’t stuck, and then read slowly through from there to find the first thing that isn’t clear. Then try some code experiments yourself, and Google around for more tutorials on whatever the issue you’re stuck with is—often you’ll find some different angle on the material might help it to click. Also, it’s expected and normal to not understand everything (especially the code) on first reading. Trying to understand the material serially before proceeding can sometimes be hard. Sometimes things click into place after you get more context from parts down the road, from having a bigger picture. So if you do get stuck on a section, try moving on anyway and make a note to come back to it later.

Remember, you don’t need any particular academic background to succeed at deep learning. Many important breakthroughs are made in research and industry by folks without a PhD, such as “Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks”—one of the most influential papers of the last decade—with over 5,000 citations, which was written by Alec Radford when he was an undergraduate. Even at Tesla, where they’re trying to solve the extremely tough challenge of making a self-driving car, CEO Elon Musk says:

: A PhD is definitely not required. All that matters is a deep understanding of AI & ability to implement NNs in a way that is actually useful (latter point is what’s truly hard). Don’t care if you even graduated high school.

What you will need to do to succeed however is to apply what you learn in this book to a personal project, and always persevere.

Your Projects and Your Mindset

Whether you’re excited to identify if plants are diseased from pictures of their leaves, auto-generate knitting patterns, diagnose TB from X-rays, or determine when a raccoon is using your cat door, we will get you using deep learning on your own problems (via pre-trained models from others) as quickly as possible, and then will progressively drill into more details. You’ll learn how to use deep learning to solve your own problems at state-of-the-art accuracy within the first 30 minutes of the next chapter! (And feel free to skip straight there now if you’re dying to get coding right away.) There is a pernicious myth out there that you need to have computing resources and datasets the size of those at Google to be able to do deep learning, but it’s not true.

So, what sorts of tasks make for good test cases? You could train your model to distinguish between Picasso and Monet paintings or to pick out pictures of your daughter instead of pictures of your son. It helps to focus on your hobbies and passions–-setting yourself four or five little projects rather than striving to solve a big, grand problem tends to work better when you’re getting started. Since it is easy to get stuck, trying to be too ambitious too early can often backfire. Then, once you’ve got the basics mastered, aim to complete something you’re really proud of!

J: Deep learning can be set to work on almost any problem. For instance, my first startup was a company called FastMail, which provided enhanced email services when it launched in 1999 (and still does to this day). In 2002 I set it up to use a primitive form of deep learning, single-layer neural networks, to help categorize emails and stop customers from receiving spam.

Common character traits in the people that do well at deep learning include playfulness and curiosity. The late physicist Richard Feynman is an example of someone who we’d expect to be great at deep learning: his development of an understanding of the movement of subatomic particles came from his amusement at how plates wobble when they spin in the air.

Let’s now focus on what you will learn, starting with the software.