In this capstone, learners will apply their deep learning knowledge and expertise to a real world challenge. They will use a library of their choice to develop and test a deep learning model. They will load and pre-process data for a real problem, build the model and validate it. Learners will then present a project report to demonstrate the validity of their model and their proficiency in the field of Deep Learning.
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Learning Outcomes:
• determine what kind of deep learning method to use in which situation
• know how to build a deep learning model to solve a real problem
• master the process of creating a deep learning pipeline
• apply knowledge of deep learning to improve models using real data
• demonstrate ability to present and communicate outcomes of deep learning projects
Course 6 of 6 in the IBM AI Engineering Professional Certificate
Syllabus
WEEK 1
Loading Data
In this module, you will get introduced to the problem that we will try to solve throughout the course. You will also learn how to load the image dataset, manipulate images, and visualize them.
WEEK 2
In this Module, you will mainly learn how to process image data and prepare it to build a classifier using pre-trained models.
WEEK 3
In this Module, in the PyTorch part, you will learn how to build a linear classifier. In the Keras part, you will learn how to build an image classifier using the ResNet50 pre-trained model.
WEEK 4
In this Module, in the PyTorch part, you will complete a peer review assessment where you will be asked to build an image classifier using the ResNet18 pre-trained model. In the Keras part, for the peer review assessment, you will be asked to build an image classifier using the VGG16 pre-trained model and compare its performance with the model that we built in the previous Module using the ResNet50 pre-trained model.
