**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 about the differences between deep learning and machine learning in this **MATLAB**® Tech Talk. Walk through several examples, and learn how to decide which method to use. The video outlines the specific workflow for solving a machine learning problem.

The video also outlines the differing requirements for machine learning and deep learning. You’ll learn about the key questions to ask before deciding between machine learning and deep learning.

The choice between machine learning or deep learning depends on your data and the problem you’re trying to solve. **MATLAB** can help you with both of these techniques – either separately or as a combined approach.

Explore the basics behind **convolutional neural networks** (CNNs) in this MATLAB® Tech Talk. Broadly, CNNs are a common deep learning architecture – but what exactly is a CNN? This video breaks down this sometimes complicated concept into easy-to-understand parts. You’ll learn about 3 concepts: local receptive fields, shared weights and biases, and activation and pooling.

The video pulls together these three concepts and shows you how to configure the layers in a CNN.

You’ll also learn about the 3 ways to train CNNs for image analysis. These include: 1.) Training the model from scratch; 2.) Using transfer learning (based on the idea that you can use knowledge of one type of problem to solve a similar problem); 3.) Using a pretrained CNN to extract features for training a machine learning model.

This demo uses **AlexNet**, a pretrained deep convolutional neural network (CNN or ConvNet) that has been trained on over a million images.

The example has two parts: setting up the camera and performing object recognition. The first part shows how to use the webcam command to acquire images from the camera. Using the drawnow command, MATLAB is able to continuously update and display images taken by the camera.

The second part illustrates how to download a pretrained deep neural network called AlexNet and use MATLAB to continuously process the camera images. AlexNet takes the image as input and provides a label for the object in the image. You can experiment with objects in your surroundings to see how accurate AlexNet is.

Today you can do this very easily with MATLAB, but even just a few years ago it would have been considered science fiction.

Watch a quick demonstration of how to use MATLAB® for transfer learning which is a practical way to apply deep learning to your problems.

This demo teaches you how to use transfer learning to re-train AlexNet, a pretrained deep convolutional neural network (**CNN** or ConvNet) to recognize snack food such as hot dogs, cup cakes and apple pie.