Deep Learning Is for Everyone

A lot of people assume that you need all kinds of hard-to-find stuff to get great results with deep learning, but as you’ll see in this book, those people are wrong. <> is a list of a few thing you absolutely don’t need to do world-class deep learning.

asciidoc
[[myths]]
.What you don't need to do deep learning
[options="header"]
|======
| Myth (don't need) | Truth
| Lots of math | Just high school math is sufficient
| Lots of data | We've seen record-breaking results with <50 items of data
| Lots of expensive computers | You can get what you need for state of the art work for free
|======

Deep learning is a computer technique to extract and transform data–-with use cases ranging from human speech recognition to animal imagery classification–-by using multiple layers of neural networks. Each of these layers takes its inputs from previous layers and progressively refines them. The layers are trained by algorithms that minimize their errors and improve their accuracy. In this way, the network learns to perform a specified task. We will discuss training algorithms in detail in the next section.

Deep learning has power, flexibility, and simplicity. That’s why we believe it should be applied across many disciplines. These include the social and physical sciences, the arts, medicine, finance, scientific research, and many more. To give a personal example, despite having no background in medicine, Jeremy started Enlitic, a company that uses deep learning algorithms to diagnose illness and disease. Within months of starting the company, it was announced that its algorithm could identify malignant tumors more accurately than radiologists.

Here’s a list of some of the thousands of tasks in different areas at which deep learning, or methods heavily using deep learning, is now the best in the world:

• Natural language processing (NLP):: Answering questions; speech recognition; summarizing documents; classifying documents; finding names, dates, etc. in documents; searching for articles mentioning a concept
• Computer vision:: Satellite and drone imagery interpretation (e.g., for disaster resilience); face recognition; image captioning; reading traffic signs; locating pedestrians and vehicles in autonomous vehicles
• Medicine:: Finding anomalies in radiology images, including CT, MRI, and X-ray images; counting features in pathology slides; measuring features in ultrasounds; diagnosing diabetic retinopathy
• Biology:: Folding proteins; classifying proteins; many genomics tasks, such as tumor-normal sequencing and classifying clinically actionable genetic mutations; cell classification; analyzing protein/protein interactions
• Image generation:: Colorizing images; increasing image resolution; removing noise from images; converting images to art in the style of famous artists
• Recommendation systems:: Web search; product recommendations; home page layout
• Playing games:: Chess, Go, most Atari video games, and many real-time strategy games
• Robotics:: Handling objects that are challenging to locate (e.g., transparent, shiny, lacking texture) or hard to pick up
• Other applications:: Financial and logistical forecasting, text to speech, and much more…

What is remarkable is that deep learning has such varied application yet nearly all of deep learning is based on a single type of model, the neural network.

But neural networks are not in fact completely new. In order to have a wider perspective on the field, it is worth it to start with a bit of history.