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)

Pooling layers

Let's see a slightly different type of layer, pooling layers, and, more specifically, we'll go in to the details of max pooling and average pooling.

Max pooling

Let's first explore how max pooling works. Similar to the convolution, we have the same parameters, the filter size is 2 x 2, the stride defines how big the step is, and we won't use any padding here:

Max pooling simply outputs the maximum of the selected values from the filter window, and, in this case, it would be nine.

It then moves the window on the right:

In this case, it moves two steps because of the stride, and outputs the maximum of the selected values, which is three.

It then moves down two steps and it outputs eight:

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