MATLAB and Simulink Training

Deep Learning with MATLAB

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Course Details

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 applications
  • Using long short-term memory networks for sequence classification and forecasting
  • Modifying common network architectures to solve custom problems
  • Improving the performance of a network by modifying training options
NVIDIA Deep Learning Institute

Deep Learning with MATLAB is endorsed by NVIDIA's Deep Learning Institute. The Deep Learning Institute offers specialized training also powered by GPUs. Check out their industry-specific content and advanced CUDA programming courses.

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

Day 2 of 2

Training a Network and Improving Performance

Objective: Understand how training algorithms work. Set training options to monitor and control training. Choose and implement modifications to training algorithm options, network architecture, or training data to improve network performance.

  • Network training
  • Training progress plots
  • Validation
  • 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

Sequence Data Classification and Generation

Objective: Build and train networks to perform classification on ordered sequences of data, such as time series or sensor data. Use recurrent networks to create sequences of predictions.

  • Long short-term memory networks
  • Sequence classification
  • Sequence preprocessing
  • Categorical sequences
  • Sequence to sequence classification
  • Sequence forecasting

Level: Intermediate


Duration: 2 days

Languages: English, 中文, 日本語, 한국어

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