Benefits of the Self-Paced Format
Hands-on exercises with automated feedback
Access to MATLAB through your web browser
Shareable progress report and course certificate
About This Course
Lessons are available in English and Japanese.
Classifying Images with Convolutional Networks
Get an overview of the course. Perform image classification using pretrained networks. Use transfer learning to train customized classification networks.
Interpreting Network Behavior
Gain insight into how a network is operating by visualizing image data as it passes through the network. Apply this technique to different kinds of images.
Build convolutional networks from scratch. Understand how information is passed between network layers and how different types of layers work.
Understand how training algorithms work. Set training options to monitor and control training.
Choose and implement modifications to training algorithm options, network architecture, or training data to improve network performance.
Create convolutional networks that can predict continuous numeric responses.
Using Deep Learning for Computer Vision
Train networks to locate and label specific objects within images.
Classifying Sequence Data with Recurrent Networks
Build and train networks to perform classification on ordered sequences of data, such as time-series or sensor data.
Classifying Categorical Sequences
Use recurrent networks to classify sequences of categorical data, such as text.
Generating Sequences of Output
Use recurrent networks to create sequences of predictions.
Looking for a Classroom Option?
Deep Learning with MATLAB is also offered in an instructor-led format.