Hands-On Java Deep Learning for Computer Vision - Softcover

Reseña del editor

Leverage the power of Java and deep learning to build production-grade Computer Vision applications

Key Features

• Build real-world Computer Vision applications using the power of neural networks
• Implement image classification, object detection, and face recognition
• Know best practices on effectively building and deploying deep learning models in Java

Book Description

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

Who this book is for

This book is for data scientists, machine learning developers and deep learning practitioners with Java knowledge who want to implement machine learning and deep neural networks in the computer vision domain. You will need to have a basic knowledge of Java programming.

Table of Contents

1. Introduction to Computer Vision and Training Neural Networks
2. Convolutional Neural Network Architectures
3. Transfer Learning and Deep CNN Architectures
4. Real-Time Object Detection
5. Creating Art with Neural Style Transfer
6. Face Recognition

Biografía del autor

Klevis Ramo is a highly motivated software engineer with a solid educational background who loves writing. 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 technologies and to improve system performance and scalability.