About the course
Learn machine learning by building advanced Java Computer Vision applications. Get hands-on with ML, ranging from handwriting recognition to self-driving cars
Although Machine Learning is an exciting world to explore, you may feel confused by all the theory and math out there. As a Java developer, you are used to telling the computer exactly what to do instead of being shown how data is generated; this makes many developers struggle to adapt to this new world of Machine Learning.
The goal of this course is to walk you through the process of efficiently training Deep Neural Networks for Computer Vision using the most modern techniques. The course is designed to get you familiar with Deep Neural Networks in order to be able to train them efficiently, customize existing state-of-the-art architectures, build real world Java applications, and get great results in a short time. You will build real-world Computer Vision applications, ranging from simple Java handwritten digit recognition to real-time Java autonomous car driving systems and face recognition.
By the end of the course you will have mastered the best practices and most modern techniques to build advanced Computer Vision Java applications and achieve production-grade accuracy.
The code bundle for this video course is available at: https://github.com/PacktPublishing/Java-Machine-Learning-for-Computer-Vision
Style and Approach
This course will teach you how to build advanced Machine Learning applications with intuitive and detailed explanations of topics, with no math background requirements. It adopts a practical approach by applying the theory to build real-world Java applications using modern practices and techniques in the Computer Vision world.
What You Will Learn
- Discover how Neural Networks work and understand the limitations and challenges developers face nowadays.
- Best practice methods and parameters and how to build Deep Neural Networks
- Hands-on real Java applications for image classification, real-time video object detection, face recognition, and art generation.
- Explore some of the most used Machine Learning Java frameworks.
- Utilize your newly acquired Machine Learning skills to help you delve into the world of data science.
Klevis Ramo is a highly motivated Software Engineer with a solid education background. He loves writing and sharing opinions on http://ramok.tech. He aims to create stable and creative solutions with performance in mind. Klevis is passionate about coding and Machine Learning with several open source contributions and is experienced at developing web services and REST API. He is always eager to learn new technology and to improve systems performance and scalability.
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