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 1
Gradient Descent
This video tells you about one of the most important building block neural networks, which is optimizers, in specific, Gradient Descent. Because Neural Networks are not just black boxes and one cannot just take and use it without understanding the underlying concept it is very important for you to watch and understand fundamental concepts. - Understand what Gradient Descent is and how it works as an optimizer - The practical application of Gradient Descent which is well suited for large datasets, Stochastic Gradient Descent - Show how we configure our neural networks to use Stochastic Descent in DL4J