YoVDO

Deep Learning with MATLAB

Offered By: MathWorks via MATLAB Academy

Tags

MATLAB Courses Deep Learning Courses Computer Vision Courses Neural Networks Courses Image Classification Courses Convolutional Networks Courses

Course Description

Overview

  • Classifying Images with Convolutional Networks: Get an overview of the course. Perform image classification using pretrained networks. Use transfer learning to train customized classification networks.
  • Interpreting Network Behavior: 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.
  • Creating Networks: Build convolutional networks from scratch. Understand how information is passed between network layers and how different types of layers work.
  • Training Networks: Understand how training algorithms work. Set training options to monitor and control training.
  • Improving Performance: Choose and implement modifications to training algorithm options, network architecture, or training data to improve network performance.
  • Spectrogram Classification Project:
  • Performing Regression: Create convolutional networks that can predict continuous numeric responses.
  • Using Deep Learning for Computer Vision: Train networks to locate and label specific objects within images.
  • Classifying Sequence Data with Recurrent Networks: Build and train networks to perform classification on ordered sequences of data, such as time series or sensor data.
  • Classifying Categorical Sequences: Use recurrent networks to classify sequences of categorical data, such as text.
  • Generating Sequences of Output: Use recurrent networks to create sequences of predictions.
  • Sequence Classification Project:
  • Conclusion: Learn next steps and give feedback on the course.

Syllabus

  • Course Overview
  • Review - Deep Learning Onramp
  • Extracting and Visualizing Activations
  • Visualizing Network Predictions
  • Review - Interpreting Network Behavior
  • Training from Scratch
  • Course Example - Landcover Classification
  • Creating Network Architectures
  • Understanding Neural Networks
  • Convolutional Layers
  • Viewing Filters
  • Review - Creating Networks
  • Understanding Network Training
  • Monitoring Training Progress
  • Validation
  • Review - Training Networks
  • Troubleshooting Methods
  • Training Options
  • Experiment Manager
  • Augmented Datastores
  • Review - Improving Performance
  • Representing Signal Data as Images
  • Project - Classify Spectrograms
  • What is Regression
  • Transfer Learning for Regression
  • Evaluating a Regression Network
  • Review - Performing Regression
  • Computer Vision Applications
  • Ground Truth
  • YOLO Object Detectors
  • Evaluating Object Detectors
  • Review - Deep Learning for Computer Vision
  • Long Short-Term Memory Networks
  • Course Example - Classify Musical Instruments
  • Structuring Sequence Data
  • Sequence Classification
  • Improving LSTM Performance
  • Review - Classifying Sequence Data with Recurrent Networks
  • Course Example - Author Identification
  • Categorical Sequences
  • Classify Text Data
  • Review - Classifying Categorical Sequences
  • Sequence-to-Sequence Classification
  • Investigate Sequence Scores
  • Sequence Forecasting
  • Review - Generating Sequences of Output
  • Project - Robot Navigation
  • Summary
  • Additional Resources
  • Survey

Taught by

Renee Bach

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