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
显示有关单元内容的信息
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
Week 1 Project: Classifying Traffic Signs with a Simple CNN•10分钟
Concept Check: Introduction to Convolutional Neural Networks•5分钟
1个讨论话题•总计5分钟
Tell us why you're here!•5分钟
Transfer Learning
第 2 单元•小时 后完成
单元详情
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
显示有关单元内容的信息
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
Week 2 Project: Performing Transfer Learning to Classify Traffic Signs•5分钟
Concept Check: Introduction to Transfer Learning•5分钟
Week 2 Quiz•10分钟
Investigating Network Behavior
第 3 单元•小时 后完成
单元详情
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.
涵盖的内容
2个视频2篇阅读材料1个作业
显示有关单元内容的信息
2个视频•总计11分钟
Interpreting Network Behavior•5分钟
Addressing Common Issues•6分钟
2篇阅读材料•总计30分钟
Investigating Network Behavior•20分钟
Addressing Common Issues Reference•10分钟
1个作业•总计30分钟
Week 3 Quiz•30分钟
Final Project: Classifying the ASL Alphabet
第 4 单元•小时 后完成
单元详情
Apply your new skills to a final project.
涵盖的内容
2个视频2篇阅读材料3个作业1个插件
显示有关单元内容的信息
2个视频•总计5分钟
Final Project: Classifying the American Sign Language Alphabet•3分钟
Course Summary•2分钟
2篇阅读材料•总计7分钟
Project Introduction: Introducing the ASL Dataset•5分钟
What's Next!?•2分钟
3个作业•总计130分钟
Project Part 2 - Train and Evaluate a Model•80分钟
Project Part 1 - Investigate and Prepare Your Data•30分钟
Project Part 3 - Classify New, Unlabeled Images•20分钟
Yes. A free license is available to learners enrolled in the course. You must have a computer capable of running MATLAB. You can view the system requirements here.
When will I have access to the lectures and assignments?
To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Certificate?
When you enroll in the course, you get access to all of the courses in the Certificate, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.