Additional information
by Yusuke Sugomori (Author), Bostjan Kaluza (Author), Fábio M. Soares (Author)
Build and run intelligent applications by leveraging key Java machine learning libraries
Key Features:
- Develop a sound strategy to solve predictive modelling problems using the most popular machine learning Java libraries.
- Explore a broad variety of data processing, machine learning, and natural language processing through diagrams, source code, and real-world applications
- This step-by-step guide will help you solve real-world problems and links neural network theory to their application
Book Description:
Machine learning applications are everywhere, from self-driving cars, spam detection, document search, and trading strategies, to speech recognitionStarting with an introduction to basic machine learning algorithms, this course takes you further into this vital world of stunning predictive insights and remarkable machine intelligence. This course helps you solve challenging problems in image processing, speech recognition, language modeling. You will discover how to detect anomalies and fraud, and ways to perform activity recognition, image recognition, and text. You will also work with examples such as weather forecasting, disease diagnosis, customer profiling, generalization, extreme machine learning and more. By the end of this course, you will have all the knowledge you need to perform deep learning on your system with varying complexity levels, to apply them to your daily work.
The course provides you with highly practical content explaining deep learning with Java, from the following Packt books:
- Java Deep Learning Essentials
- Machine Learning in Java
- Neural Network Programming with Java, Second Edition
What You Will Learn:
- Get a practical deep dive into machine learning and deep learning algorithms
- Explore neural networks using some of the most popular Deep Learning frameworks
- Dive into Deep Belief Nets and Stacked Denoising Autoencoders algorithms
- Apply machine learning to fraud, anomaly, and outlier detection
- Experiment with deep learning concepts, algorithms, and the toolbox for deep learning
- Select and split data sets into training, test, and validation, and explore validation strategies
- Apply the code generated in practical examples, including weather forecasting and pattern recognition
Who this book is for:
This course is intended for data scientists and Java developers who want to dive into the exciting world of deep learning. It will get you up and running quickly and provide you with the skills you need to successfully create, customize, and deploy machine learning applications in real life.
