From the series: Introduction to Deep Learning
Shyamal Patel, MathWorks
Explore deep learning fundamentals in this MATLAB® Tech Talk. You’ll learn why deep learning has become so popular, and walk through 3 concepts: what deep learning is, how it is used in the real world, and how you can get started.
Deep learning is a machine learning technique that learns features and tasks directly from data. This data can include images, text, or sound. The video uses an example image recognition problem to illustrate how deep learning algorithms learn to classify input images into appropriate categories. Lastly, the video explores the three reasons why deep learning has surged in popularity over the last five years.
Learn more about using MATLAB for deep learning.
Deep learning is getting lots of attention lately, and for good reason. It’s making a big impact in areas such as computer vision and natural language processing.
In this video series, we’ll help you understand why it’s become so popular, and we'll address three concepts:
So, what is deep learning?
Deep learning is a machine learning technique that learns features and tasks directly from data. Data can be images, text, or sound. In this video, I’ll be using images, but these concepts can be used for other types of data, too.
Machine learning teaches computers to do what comes naturally to humans: learn from experience. Most machine learning workflows use training images that represent examples of what the system is trying to learn.
Computers learn from these images through feature extraction. This is where information like edges, texture, and color is extracted to form a reduced representation that easily describes the object. These features are then used by a machine learning classifier to learn a task. Deep learning is often referred to as end-to-end learning.
Let’s look at an example:
Say I have a set of images, and I want to recognize which category of objects each image belongs to – cars, trucks, or boats.
I start with a labeled set of images, or training data. The labels correspond to the desired output of the task. The deep learning algorithm needs these labels, as they tell the algorithm about the specific features and objects in the image.
The deep learning algorithm then learns how to classify input images into the desired categories. We use the term "end-to-end learning" because the task is learned directly from the data.
Another example is a robot learning how to control the movement of its arm to pick up a specific object. In this case, the task being learned is how to pick up an object given an input image.
Many of the techniques used in deep learning today have been around for decades. For example, deep learning has been used to recognize handwritten postal codes in the mail service since the 1990’s.
The use of deep learning has surged over the last 5 years. This is primarily due to three factors:
1. Deep learning methods are now more accurate than people are at classifying images.
2. GPUs enable us to now train deep networks in less time.
3. Large amounts of labeled data required for deep learning has become accessible over the last few years.
Most deep learning methods use neural network architectures. This is why you often hear deep learning models referred to as deep neural networks. One of the most popular types of deep neural networks is known as a convolutional neural network. It is especially well suited for working with image data. The term “deep” usually refers to the number of hidden layers in the neural network. Traditional neural networks only contain 2-3 hidden layers, while some recent deep networks have as many as 150.
Here are a few examples that you can try with MATLAB:
I hope you found this overview helpful. To find out more, you can visit our website at mathworks.com/deep-learning.
Recorded: 24 Mar 2017