MATLAB & Simulink


Deep Learning with MATLAB

Course Highlights

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


Topics include:

  • 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.



Upcoming Program

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Techsource Systems is
Mathworks Sole and Authorised Distributor and Training Partner

Course Prerequisite

MATLAB Fundamentals 

Course Benefits

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. 

Course Outline

Day 1 of 2

Transfer Learning for Image Classification

Objective: 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

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.

  • Activations

  • Feature extraction for machine learning


Creating Networks

Objective: 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


Training a Network

Objective: Understand how training algorithms work. Set training options to monitor and control training.

  • Network training

  • Training progress plots

  • Validation


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.

  • Training options

  • Directed acyclic graphs

  • Augmented datastores


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

  • Object detection


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

  • Sequence classification

  • Sequence preprocessing

  • Categorical sequences


Generating Sequences of Output

Objective: Use recurrent networks to create sequences of predictions.

  • Sequence to sequence classification

  • Sequence forecasting