Deep Learning with Java [Video]

More Information
Learn
  • Extract features from unstructured data using ND4J
  • Use DL4J to perform fast and efficient deep learning training
  • Perform automatic speech recognition with DL
  • Use RNN with DL to achieve more precise results based on previous history
  • Process image data using multiple layers with DL4J
  • Use Word2Vect to perform feature extraction on text data
  • Predict using classification with a multilayered approach
About

Deep learning (DL) is used across a broad range of industries as the fundamental driver of AI. Being able to apply deep learning with Java will be a vital and valuable skill, not only within the tech world but also the wider global economy, which depends upon solving problems with higher accuracy and much more predictability than other AI techniques could provide.

This step-by-step, practical tutorial teaches you how to implement key concepts and adopts a hands-on approach to key algorithms to help you develop a greater understanding of deep learning. You will learn how to use the DL4J library and apply deep learning to a range of real-world use cases. This course will also help you solve challenging problems in image processing, speech recognition, and natural language modeling; it will make you rethink what you can do with Java, showing you how to use it for truly cutting-edge predictive insights.

By the end of this course, you'll be ready to tackle deep learning with Java. Whether you come from a data science background or are a Java developer, you will become part of the deep learning revolution!

The code bundle for this course is available at https://github.com/PacktPublishing/Deep-Learning-with-Java

Features
  • Learn key algorithms needed to enhance your understanding of deep learning
  • Use Java and deep neural networks to solve problems with the help of image processing, speech recognition, and natural language modeling
  • Use the DL4J library and apply deep learning concepts to real-world use cases
Course Length 1 hour 54 minutes
ISBN 9781789806373
Date Of Publication 12 Jul 2019