Convolutional Neural Networks
Unlock the power of Convolutional Neural Networks! Learn how to process images, and classify objects from images. Apply your new skills to object and face recognition!
This title is part of the Deep Learning Mini-Degree
Interested in face recognition, fingerprint matching or object recognition? Want to learn tools that can be used to develop the AI of self-driving cars? Discover how to implement your own algorithms and understand why they work in this course about Convolutional Neural Networks (CNNs)!
Let’s take a look at what you’ll learn in this course:
What you’ll learn
- Understand what an image is – how it’s stored in the memory of a computer, differences between a grayscale image and a color image
- Learn about the challenges of image classification and how to address them – differences in size or resolution, object position variance, occlusion and more!
- The K-NN (nearest neighbor) algorithm
- Using filters (kernels) for feature detection – detect a specific object featured in a given image
- CNN Introduction – reasoning and architecture
- CNN Layers – Convolutions, Pooling, Fully-Connected Layers
Case Studies on the architectures of various public CNNs:
About the Deep Learning Mini-Degree
The Deep Learning Mini-Degree is an on-demand learning curriculum composed of 6 professional-grade courses geared towards teaching you how to solve real-world problems and build innovative projects using Machine Learning and Python. Learn and understand the fundamentals necessary to build the next generation of intelligent applications and software, with concepts and theory that can be applied across technology and frameworks.
Challenge yourself by joining this exciting project-based curriculum and gain the knowledge and abilities required to succeed in this brand new industry. No prior experience with AI or Machine Learning is necessary to join. However, basic to intermediate Python skills are assumed in all of the courses.
- Intermediate Python programming skills
- Familiarity with Artificial Neural Networks