Deep Learning with MATLAB
Who Should Attend?
Engineers, professionals, researchers who are involved in machine learning design for image processing
This course provides a comprehensive introduction to practical deep learning using MATLAB®. Attendees will learn how to create, train, and evaluate different kinds of deep neural networks.
- Import image and sequence data
- Use convolutional neural networks for image classification, regression, and object detection
- Use long short-term memory networks for sequence classification and forecasting
- Modify common network architectures to solve custom problems
- Improve performance of a network by modifying training options
The aim of this training is to provide participants with comprehensive introduction on deep learning with Neural Network toolbox for image processing applications.
Classifying Images with Convolution Networks
Objectives: Get an overview of the course. Perform image classification using pretrained networks. Use transfer learning to train customized classification networks.
- Pretrained networks
- Image datastores
- Transfer learning
- Network evaluation
Interpreting Network Behavior
Objectives: 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.
- Images from signal data
- Feature extraction for machine learning
Objectives: Build convolutional networks from scratch. Understand how information is passed between network layers and how different types of layers work.
- Training from scratch
- Neural networks
- Convolution layers and filters
Objectives: Understand how training algorithms work. Set training options to monitor and control training.
- Network training
- Training progress plots
Objectives: Choose and implement modifications to training algorithm options, network architecture, or training data to improve network performance.
- Training options
- Augmented datastores
- Directed acyclic graphs
Objectives: Create convolutional networks that can predict continuous numeric responses.Training options
- Transfer learning for regression
- Evaluation metrics for regression networks
Detecting Objects in Images
Objectives: Train networks to locate and label specific objects within images.
- Object detection
Classifying Sequence Data with Recurrent Networks
Objectives: Build and train networks to perform classification on ordered sequences of data, such as time series or sensor data.
- Long short-term memory networks
- Sequence classification
- Sequence preprocessing
Classifying Categorical Sequences
Objectives: Use recurrent networks to classify sequences of categorical data, such as text.
- Categorical sequences
- Text classification
Generating Sequences of Output
Objectives: Use recurrent networks to create sequences of predictions.
- Sequence to sequence classification
- Sequence forecasting
Drop us your entry if you are interested to join this course.
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