About the course
Gain a working knowledge of advanced machine learning; Video Course
Python’s wealth of powerful packages along with its clear syntax make state-of-the art computer vision and machine learning accessible to developers with a variety of backgrounds. This video course will equip you with the tools and skills to utilize the latest and greatest algorithms in computer vision, making applications that weren’t possible until recent years.
In this course, you’ll continue to use TensorFlow and extend it to generate full captions from images. Later, you’ll see how to read text from license plates from real-world images using Google’s Tesseract Software. Finally, you’ll see how to track human body poses using “DeeperCut” within TensorFlow.
At the end of this course, you’ll develop an application that can estimate human poses within images and will be able to take on the world with best practices in computer vision with machine learning.
The code bundle for this video course is available at - https://github.com/PacktPublishing/Advanced-Computer-Vision-Projects
Style and Approach
A project-based approach that will enable you to rapidly deploy advanced machine learning computer vision solutions in your work.
What You Will Learn
- Apply LSTMs to automated image captioning
- Know how to read text from real-world images
- See how to extract human pose data from images
- Understand the TensorFlow workflow model
Matthew Rever is an image processing and computer vision engineer at a major national laboratory. He has years of experience automating the analysis of complex scientific data as well as controlling sophisticated instruments. He has applied computer vision technology to save a great many hours of valuable human labor. He is also enthusiastic about making the latest developments in computer vision accessible to developers of all backgrounds.
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