Book Image

Hands-On Java Deep Learning for Computer Vision

By : Klevis Ramo
Book Image

Hands-On Java Deep Learning for Computer Vision

By: Klevis Ramo

Overview of this book

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.
Table of Contents (8 chapters)

Using residual networks for image recognition

In this section, we're going to see what happens when we train deep networks, which has many layers, probably over 30 or maybe even over 100. Then, we'll present residual networks as a solution for scaling too many layers, along with an architecture example with state-of-the-art accuracy.

Deep network performance

In theory, it's clear that the more layers we add to the neuron network, the better it is. This is the case with the green line, as shown in the following graph. As soon as we add more layers, we'll see the error rate go down, ideally to zero. Unfortunately, in practice, we see this happening only partially. It's true that the error rate goes down...