MATLAB & Simulink
DATA ANALYSIS AND MACHINE LEARNING
Deep Learning with MATLAB
This two-day 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 . The instructor-led training uses NVIDIA GPUs to accelerate network training
Importing image and sequence data
Using convolutional neural networks for image classification, regression, and other image appplications
Using long short-term memory networks for sequence classification and forecasting
Modifying common network architectures to solve custom problems
Improving performance of a network by modifying training options
Who Should Attend
Engineers, professionals, researchers who are involved in machine learning design for image processing.
Techsource Systems is
Mathworks Sole and Authorised Distributor and Training Partner
Upon the completion of the course, the participants will be able to
- perform image classification, regression and object detection
- perform sequence classification and forecasting
- modify common network architecture to solve custom problems - improve performance of a network.
Day 1 of 2
Transfer Learning for Image Classification
Objective: Perform image classification using pretrained networks. Use transfer learning to train customized classification networks.
Interpreting Network Behavior
Objective: 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.
Feature extraction for machine learning
Objective: Build convolutional networks from scratch. Understand how information is passed between network layers and how different types of layers work.
Training from scratch
Convolution layers and filters
Training a Network
Objective: Understand how training algorithms work. Set training options to monitor and control training.
Training progress plots
Day 2 of 2
Improving Network Performance
Objective: Choose and implement modifications to training algorithm options, network architecture, or training data to improve network performance.
Directed acyclic graphs
Performing Image Regression
Objective: Create convolutional networks that can predict continuous numeric responses.
Transfer learning for regression
Evaluation metrics for regression networks
Using Deep Learning for Computer Vision
Objective: Train networks to locate and label specific objects within images.
Image application workflow
Classifying Sequence Data
Objective: Build and train networks to perform classification on ordered sequences of data, such as time series or sensor data.
Long short-term memory networks
Generating Sequences of Output
Objective: Use recurrent networks to create sequences of predictions.
Sequence to sequence classification