Introduction to Deep Learning for Computer Vision

本课程是多个项目的一部分。

位教师：Mehdi Alemi

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深入了解一个主题并学习基础知识。

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4个模块

深入了解一个主题并学习基础知识。

19 条评论

初级等级

推荐体验

9 小时完成

灵活的计划

自行安排学习进度

您将学到什么

Develop a strong foundation in deep learning for image analysis
Retrain common models like GoogLeNet and ResNet for specific applications
Investigate model behavior to identify errors, determine potential fixes, and improve model performance
Complete a real-world project to practice the entire deep learning workflow

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作业

9 项作业

授课语言：英语（English）

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该课程共有4个模块

Starting with zero deep learning knowledge, this foundational course will guide you to effectively train cutting-edge models for image classification purposes. From analyzing medical images to recognizing traffic signs, classification is important for many applications. Classification models also serve as the backbone for more complicated object detection models. Through hands-on projects, you will train and evaluate models to classify street signs and identify the letters of American Sign Language. By completing this course, you will develop a strong foundation in deep learning for image analysis and will be equipped with the skills to tackle real-world computer vision challenges.

By the end of this course, you will be able to: • Explain how deep learning networks find image features and make predictions • Retrain common models like GoogLeNet and ResNet for specific applications • Investigate model behavior to identify errors and determine potential fixes • Improve model performance by tuning hyperparameters • Complete the entire deep learning workflow in a final project For the duration of the course, you will have free access to MATLAB, software used by top employers worldwide. The courses draw on the applications using MATLAB, so you spend less time coding and more time applying deep learning concepts.

Learn the key components of convolutional neural networks and train a simple classification model

涵盖的内容

5个视频6篇阅读材料2个作业1个讨论话题

5个视频总计31分钟

Deep Learning for Computer Vision 3分钟
Introduction to Deep Learning for Computer Vision 2分钟
Introduction to Convolutional Neural Networks 8分钟
Preparing Your Data for Classification 5分钟
Creating and Training a CNN for Classification 13分钟

6篇阅读材料总计127分钟

Meet Your Instructors 5分钟
Prerequisite Knowledge 2分钟
Download and Install MATLAB 15分钟
Course Files 15分钟
Creating and Training a CNN 45分钟
Project: Introduction to the Traffic Signs Dataset 45分钟

2个作业总计15分钟

Concept Check: Introduction to Convolutional Neural Networks 5分钟
Week 1 Project: Classifying Traffic Signs with a Simple CNN 10分钟

1个讨论话题总计5分钟

Tell us why you're here! 5分钟

Retraining networks with new data is the most common way to apply deep learning in industry. In this module, you'll retrain common networks, set appropriate values for training options, and compare results from different models.

涵盖的内容

4个视频4篇阅读材料3个作业

4个视频总计24分钟

Introduction to Transfer Learning 4分钟
Performing Transfer Learning for Classification 6分钟
Common Training Options 8分钟
Training and Comparing Models with Experiment Manager 7分钟

4篇阅读材料总计72分钟

Using and Comparing Pre-Trained Models 20分钟
"Performing Transfer Learning for Classification" Video Code 2分钟
Common Training Options Reference 20分钟
Introducing the Week 2 Project 30分钟

3个作业总计20分钟

Concept Check: Introduction to Transfer Learning 5分钟
Week 2 Quiz 10分钟
Week 2 Project: Performing Transfer Learning to Classify Traffic Signs 5分钟

Explaining how models make predictions is increasingly important. In this module, you'll use confidence scores and visualizations to determine what regions of an image the model is using to make predictions. You'll also identify common errors and adjust training options to improve performance.