Hands-On Java Deep Learning for Computer Vision: Implement machine learning and neural network methodologies to perform computer vision-related tasks

Hands-On Java Deep Learning for Computer Vision: Implement machine learning and neural network methodologies to perform computer vision-related tasks
Hands-On Java Deep Learning for Computer Vision: Implement machine learning and neural network methodologies to perform computer vision-related tasks by Klevis Ramo
English | 2019 | ISBN: 1789613964 | 260 Pages | True PDF, EPUB | 200 MB

Leverage the power of Java and deep learning to build production-grade Computer Vision applications
Although machine learning is an exciting world to explore, you may feel confused by all of its theoretical aspects. As a Java developer, you will be used to telling the computer exactly what to do, instead of being shown how data is generated; this causes many developers to struggle to adapt to machine learning.
The goal of this book is to walk you through the process of efficiently training machine learning and deep learning models for Computer Vision using the most up-to-date techniques. The book is designed to familiarize you with neural networks, enabling you to train them efficiently, customize existing state-of-the-art architectures, build real-world Java applications, and get great results in a short space of time. You will build real-world Computer Vision applications, ranging from a simple Java handwritten digit recognition model to real-time Java autonomous car driving systems and face recognition models.
By the end of this book, you will have mastered the best practices and modern techniques needed to build advanced Computer Vision Java applications and achieve production-grade accuracy.
What you will learn

  • Discover neural networks and their applications in Computer Vision
  • Explore the popular Java frameworks and libraries for deep learning
  • Build deep neural networks in Java
  • Implement an end-to-end image classification application in Java
  • Perform real-time video object detection using deep learning
  • Enhance performance and deploy applications for production