Deep Learning with MATLAB
Learn the theory and practice of building deep neural networks with real-life image and sequence data.
Prerequisites: MATLAB Fundamentals and Deep Learning Onramp
This course is included with select licenses.
Learn the theory and practice of building deep neural networks with real-life image and sequence data.
Prerequisites: MATLAB Fundamentals and Deep Learning Onramp
This course is included with select licenses.
Shareable progress report and course certificate
Lessons are available in English and Japanese.
Get an overview of the course. Perform image classification using pretrained networks. Use transfer learning to train customized classification networks.
30 mins
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.
45 mins
Build convolutional networks from scratch. Understand how information is passed between network layers and how different types of layers work.
45 mins
Understand how training algorithms work. Set training options to monitor and control training.
30 mins
Choose and implement modifications to training algorithm options, network architecture, or training data to improve network performance.
30 mins
15 mins
Create convolutional networks that can predict continuous numeric responses.
30 mins
Train networks to locate and label specific objects within images.
45 mins
Build and train networks to perform classification on ordered sequences of data, such as time-series or sensor data.
45 mins
Use recurrent networks to classify sequences of categorical data, such as text.
30 mins
Use recurrent networks to create sequences of predictions.
45 mins
15 mins
Learn core MATLAB functionality for data analysis, modeling, and programming.
Get started quickly using deep learning methods to perform image recognition.
Explore data and build predictive models.
Deep Learning with MATLAB is also offered in an instructor-led format.