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.
AI Capstone Project with Deep Learning
AI Capstone Project with Deep Learning
This course is part of IBM AI Engineering Professional Certificate
Instructors: Alex Aklson
Sponsored by University of Texas at Austin
27,104 already enrolled
Gain insight into a topic and learn the fundamentals.
4.5
(582 reviews)
Advanced level
Designed for those already in the industry
16 hours to complete
3 weeks at 5 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
Build a deep learning model to solve a real problem.
Execute 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.
Skills you'll gain
Details to know
Earn a career certificate
Add to your LinkedIn profile
Assessments
7 assignments
Taught in English
See how employees at top companies are mastering in-demand skills
Build your Machine Learning expertise
This course is part of the IBM AI Engineering Professional Certificate
When you enroll in this course, you'll also be enrolled in this Professional Certificate.
- 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 from IBM
Earn a career certificate
Add this credential to your LinkedIn profile, resume, or CV
Share it on social media and in your performance review
There are 4 modules in this course
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.
What's included
4 videos3 assignments2 app items
In this Module, you will mainly learn how to process image data and prepare it to build a classifier using pre-trained models.
What's included
1 video2 assignments2 app items
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.
What's included
1 video2 assignments2 app items
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.
What's included
1 video2 peer reviews1 app item
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