MATLAB & Simulink

DATA ANALYSIS AND MACHINE LEARNING

Deep Learning with MATLAB

Course Highlights

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 . Topics include:

  • 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

Course Objectives

The aim of this training is to provide participants with comprehensive introduction on deep learning with Neural Network toolbox for image processing applications.

Course Benefits

Partners 

Upcoming Program

Techsource Systems is
Mathworks Sole and Authorised Distributor and Training Partner

The aim of the training is to provide comprehensive introduction on deep learning with MATLAB for image processing.

Who Must Attend

Engineers, professionals, researchers who are involved in machine learning design for image processing.

Course Outline

Classifying Images with Convolutional Networks

 

Objective: 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

 

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

  • Images from signal data

  • 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 Networks

 

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

  • Network training

  • Training progress plots

  • Validation

 

Improving Performance

 

Objective: Choose and implement modifications to training algorithm options, network architecture, or training data to improve network performance.

  • Training options

  • Augmented datastores

  • Directed acyclic graphs

 

Performing Regression

 

Objective: Create convolutional networks that can predict continuous numeric responses.

  • Transfer learning for regression

  • Evaluation metrics for regression networks

 

Detecting Objects in Images

 

Objective: Train networks to locate and label specific objects within images.

  • Object detection

 

Classifying Sequence Data with Recurrent Networks

 

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

 

Classifying Categorical Sequences

Objective: Use recurrent networks to classify sequences of categorical data, such as text.

  • Categorical sequences

  • Text classification

 

Generating Sequences of Output

 

Objective: Use recurrent networks to create sequences of predictions.

  • Sequence to sequence classification

  • Sequence forecasting

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