Book Image

Getting Started with Java Deep Learning [Video]

By : Sercan Karaoglu
Book Image

Getting Started with Java Deep Learning [Video]

By: Sercan Karaoglu

Overview of this book

<p>AI and deep learning are transforming the way we understand software, making computers more intelligent than we could even imagine just a decade ago. It is the technology behind self-driven cars, intelligent personal assistant computers, and decision support systems. Deep learning algorithms are being used across a broad range of industries. As the fundamental driver of AI, being able to tackle deep learning with Java is going to be a vital and valuable skill, not only within the tech world, but also for the wider global economy that depends upon knowledge and insight for growth and success.</p> <p>You will learn how to install the environment, where Git is used as version control, Eclipse or IntelliJ as an IDE, and mostly Gradle with a little bit of Maven as a build tool. You will learn how to use the DL4J and apply deep learning to a range of real-world use cases. You will then be introduced to Neural networks and later you will learn how to implement them. You will also be given an insight about various deep learning algorithms. You will then be trained to tune Apache Spark.</p> <p>By the end of the video course, you’ll be ready to tackle deep learning with Java. Wherever you’ve come from—whether you’re a data scientist or Java developer—you will become a part of the deep learning revolution!</p> <h1>Style and Approach</h1> <p>This is a step-by-step, practical tutorial that discusses key concepts. The book offers a hands-on approach to key algorithms to help you develop a greater understanding of deep learning. It is packed with implementations from scratch, with detailed explanation that make the concepts easy to understand and follow.</p>
Table of Contents (5 chapters)
Chapter 3
Implementing Neural Nets
Content Locked
Section 2
Multilayer Perceptron
Up to now, we talked about some linear models. These are the simplest models and works okay for most cases. However, sometimes there is a nonlinear relationship between features and the target values. In this section, we are going to introduce how we solve when nonlinear relationship is seen in the data. - Explain the model by recalling logistic regression - Show how to add layers for more complex models - Show tips and tricks about training MLP