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Introduction to Deep Learning for Computer Vision

 

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

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.

Join Free: Introduction to Deep Learning for Computer Vision






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