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.
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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.
This course is part of the Deep Learning for Computer Vision Specialization.
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
Syllabus
Introduction to Deep Learning with Images
Learn the key components of convolutional neural networks and train a simple classification model
Transfer Learning
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.
Investigating Network Behavior
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.
Final Project: Classifying the ASL Alphabet
Apply your new skills to a final project.
