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Introduction to Deep Learning for Computer Vision
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  • Modules
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  3. Machine Learning
MathWorks

Introduction to Deep Learning for Computer Vision

This course is part of multiple programs.

This course is part of multiple programs

Deep Learning for Computer Vision Specialization
MathWorks Computer Vision Engineer Professional Certificate
Mehdi Alemi
Amanda Wang
Megan Thompson

Instructors: Mehdi Alemi

Instructors

Mehdi Alemi
Mehdi Alemi
MathWorks
4 Courses•5,584 learners
Amanda Wang
Amanda Wang
MathWorks
9 Courses•35,345 learners
Megan Thompson
Megan Thompson
MathWorks
9 Courses•35,345 learners
Matt Rich
Matt Rich
MathWorks
13 Courses•60,632 learners
Brandon Armstrong
Brandon Armstrong
MathWorks
17 Courses•91,982 learners

3,550 already enrolled

Included with Coursera Plus

•Learn more
4 modules
Gain insight into a topic and learn the fundamentals.
4.9

(11 reviews)

Beginner level

Recommended experience

Recommended experience

Beginner level

We recommend some prior experience working with images and MATLAB. If you’re new to image data, enroll in Introduction to Image Processing.

8 hours to complete
Flexible schedule
Learn at your own pace

4 modules
Gain insight into a topic and learn the fundamentals.
4.9

(11 reviews)

Beginner level

Recommended experience

Recommended experience

Beginner level

We recommend some prior experience working with images and MATLAB. If you’re new to image data, enroll in Introduction to Image Processing.

8 hours to complete
Flexible schedule
Learn at your own pace
  • About
  • Outcomes
  • Modules
  • Recommendations
  • Testimonials

What you'll learn

  • 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

Skills you'll gain

  • Matlab
  • Data Processing
  • Computer Vision
  • Artificial Neural Networks
  • Machine Learning Methods
  • Performance Tuning
  • Predictive Modeling
  • Image Analysis
  • Classification And Regression Tree (CART)
  • Applied Machine Learning
  • Artificial Intelligence and Machine Learning (AI/ML)
  • Deep Learning

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

9 assignments

Taught in English

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Build your subject-matter expertise

This course is available as part of
When you enroll in this course, you'll also be asked to select a specific program.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate

There are 4 modules in this course

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

What's included

5 videos6 readings2 assignments1 discussion prompt

5 videos•Total 31 minutes
  • Deep Learning for Computer Vision•2 minutes•Preview module
  • Introduction to Deep Learning for Computer Vision•1 minute
  • Introduction to Convolutional Neural Networks•8 minutes
  • Preparing Your Data for Classification•4 minutes
  • Creating and Training a CNN for Classification•13 minutes
6 readings•Total 127 minutes
  • Meet Your Instructors•5 minutes
  • Prerequisite Knowledge•2 minutes
  • Download and Install MATLAB•15 minutes
  • Course Files•15 minutes
  • Creating and Training a CNN•45 minutes
  • Project: Introduction to the Traffic Signs Dataset•45 minutes
2 assignments•Total 15 minutes
  • Concept Check: Introduction to Convolutional Neural Networks•5 minutes
  • Week 1 Project: Classifying Traffic Signs with a Simple CNN•10 minutes
1 discussion prompt•Total 5 minutes
  • Tell us why you're here!•5 minutes

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.

What's included

4 videos4 readings3 assignments

4 videos•Total 24 minutes
  • Introduction to Transfer Learning•3 minutes•Preview module
  • Performing Transfer Learning for Classification•5 minutes
  • Common Training Options•7 minutes
  • Training and Comparing Models with Experiment Manager•7 minutes
4 readings•Total 72 minutes
  • Using and Comparing Pre-Trained Models•20 minutes
  • "Performing Transfer Learning for Classification" Video Code•2 minutes
  • Common Training Options Reference•20 minutes
  • Introducing the Week 2 Project•30 minutes
3 assignments•Total 20 minutes
  • Concept Check: Introduction to Transfer Learning•5 minutes
  • Week 2 Quiz•10 minutes
  • Week 2 Project: Performing Transfer Learning to Classify Traffic Signs•5 minutes

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.

What's included

2 videos2 readings1 assignment

2 videos•Total 10 minutes
  • Interpreting Network Behavior•4 minutes•Preview module
  • Addressing Common Issues•5 minutes
2 readings•Total 30 minutes
  • Investigating Network Behavior•20 minutes
  • Addressing Common Issues Reference•10 minutes
1 assignment•Total 30 minutes
  • Week 3 Quiz•30 minutes

Apply your new skills to a final project.

What's included

2 videos2 readings3 assignments1 plugin

2 videos•Total 5 minutes
  • Final Project: Classifying the American Sign Language Alphabet•2 minutes•Preview module
  • Course Summary•2 minutes
2 readings•Total 7 minutes
  • Project Introduction: Introducing the ASL Dataset•5 minutes
  • What's Next!?•2 minutes
3 assignments•Total 130 minutes
  • Project Part 1 - Investigate and Prepare Your Data•30 minutes
  • Project Part 3 - Classify New, Unlabeled Images•20 minutes
  • Project Part 2 - Train and Evaluate a Model•80 minutes
1 plugin•Total 15 minutes
  • Complete the Course Survey•15 minutes

Earn a career certificate

Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.

Instructors

Mehdi Alemi
Mehdi Alemi
MathWorks
4 Courses•5,584 learners

Instructors

Mehdi Alemi
Mehdi Alemi
MathWorks
4 Courses•5,584 learners
Amanda Wang
Amanda Wang
MathWorks
9 Courses•35,345 learners
Megan Thompson
Megan Thompson
MathWorks
9 Courses•35,345 learners
Matt Rich
Matt Rich
MathWorks
13 Courses•60,632 learners
Brandon Armstrong
Brandon Armstrong
MathWorks
17 Courses•91,982 learners

Offered by

MathWorks

Offered by

MathWorks

Accelerating the pace of discovery, innovation, development, and learning in engineering and science.

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Frequently asked questions

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 hereOpens in a new tab.

Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:

  • The course may not offer an audit option. 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.

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. If you only want to read and view the course content, you can audit the course for free.

If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policyOpens in a new tab.

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