Deep Learning with MATLAB

교육과정 모듈

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.

강의 내용:

Course Overview

Deep Learning Basics

Understanding Convolutional Neural Networks

Understand how information is passed between network layers and how different types of layers work.

강의 내용:

Understanding Neural Networks

Convolutional Layers

Viewing Filters

Viewing the Activations of Different Layers

Review - Understanding Convolutional Neural Networks

Creating and Training Networks

Train networks from scratch. Understand how training algorithms work. Set training options to monitor and control training.

강의 내용:

Training from Scratch

Creating Network Architectures

Understanding Network Training

Monitoring Training Progress

Validation

Review - Creating and Training Networks

Improving Performance

Choose and implement modifications to training algorithm options and training data to improve network performance.

강의 내용:

Troubleshooting Methods

Visualizing Network Predictions

Augmented Datastores

Training Options

Experiment Manager

Review - Improving Performance

Image Classification Project

Bring together image classification concepts that you have learned with a project.

강의 내용:

Project - Classify Fashion Images

Performing Regression

Create convolutional networks that can predict continuous numeric responses.

강의 내용:

What is Regression

Transfer Learning for Regression

Evaluating a Regression Network

Review - Performing Regression

Using Deep Learning for Computer Vision

Train networks to locate and label specific objects within images.

강의 내용:

Computer Vision Applications

Ground Truth

YOLO Object Detectors

Evaluating Object Detectors

Review - Deep Learning for Computer Vision

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.

강의 내용:

Course Example - Classify Flooding Level

Managing Collections of Signal Data

Long Short-Term Memory Networks

Sequence Classification

Improving LSTM Performance

Review - Classifying Sequence Data with Recurrent Networks

Classifying Sequences of Output

Use recurrent networks to create sequences of predictions.

강의 내용:

Course Example - Classify Regions of Flooding Levels

Labeling Regions of Interest

Sequence-to-Sequence Classification

Review - Classifying Sequences of Output

Sequence Classification Project

Bring together signal classification concepts that you have learned with a project.

강의 내용:

Project - Robot Navigation

Conclusion

Learn next steps and give feedback on the course.

강의 내용:

Summary

Additional Resources

Survey

교육과정 모듈

Classifying Images with Convolutional Networks

Understanding Convolutional Neural Networks

Creating and Training Networks

Improving Performance

Image Classification Project

Performing Regression

Using Deep Learning for Computer Vision

Classifying Sequence Data with Recurrent Networks

Classifying Sequences of Output

Sequence Classification Project

Conclusion

언어

자동화된 피드백이 제공되는 실습 연습문제
웹 브라우저를 통해 MATLAB 이용
공유 가능한 진도 보고서 및 교육과정 수료증

Image Processing with MATLAB

Reinforcement Learning Onramp

Deep Learning Onramp

Deep Learning with MATLAB

교육과정 모듈

Classifying Images with Convolutional Networks

Understanding Convolutional Neural Networks

Creating and Training Networks

Improving Performance

Image Classification Project

Performing Regression

Using Deep Learning for Computer Vision

Classifying Sequence Data with Recurrent Networks

Classifying Sequences of Output

Sequence Classification Project

Conclusion

언어

자동화된 피드백이 제공되는 실습 연습문제웹 브라우저를 통해 MATLAB 이용공유 가능한 진도 보고서 및 교육과정 수료증

Image Processing with MATLAB

Reinforcement Learning Onramp

Deep Learning Onramp

자동화된 피드백이 제공되는 실습 연습문제
웹 브라우저를 통해 MATLAB 이용
공유 가능한 진도 보고서 및 교육과정 수료증