This is the code repository for Java High Performance [Video], published by Packt. It contains all the supporting project files necessary to work through the video course from start to finish.
Deep Learning is 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 solving problems with a higher accuracy and much more predictability than any other AI techniques.
This is a step-by-step, practical tutorial that teaches you the implementation of the key concepts. The course offers a hands-on approach to key algorithms to help you develop a greater understanding of deep learning. You will not only apply Deep Learning to a range of real-world use cases but also learn how to use the DL4J library to extract features from data. You will also be able to solve challenging problems in image processing, speech recognition, natural language modeling. This course 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 and leverage the most powerful Java DL libraries for creating your neural networks and models.
- 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
This course has the following software requirements:
For successful completion of this course, students will require the computer systems with at least the following:
• IntelliJ IDEA
• Java JDK 8 or later
For an optimal experience with hands-on labs and other practical activities, we recommend the following configuration:
• OS: Windows, MacOSX
• Processor: Intel or compatible
• Memory: 16 GB RAM
• Storage: 200 MB or more hard disk
• Video Card: 256MB Video Memory