Solve complex data science tasks through practical applications of deep learning with Java
About This Book
- Introduces modern machine learning techniques, and dives into deep learning algorithms for practical applications
- Build from scratch and library-oriented implementations with Java to fully grasp the structure of deep learning
- Get to grips with latest deep learning techniques and learn to implement the core mathematics needed
Who This Book Is For
This book is intended for data scientists and Java developers who want to dive into the exciting world of deep learning. It would also be good for machine learning users who intend to leverage deep learning in their projects, working within a big data environment.
What You Will Learn
- Get a practical deep dive into machine learning and deep learning algorithms
- Implement machine learning algorithms related to deep learning
- Overcome the difficulties of neural networks using deep learning
- Dive into Deep Belief Nets and Stacked Denoising Autoencoders algorithms
- Discover more deep learning algorithms with Dropout and Convolutional Neural Networks
- Gain an insight into the deep learning library DL4J and its practical uses
- Get to know device strategies to use deep learning algorithms and libraries in the real world
- Explore deep learning further with Theano and Caffe
In Detail
With an increasing interest in AI around the world, deep learning has attracted a great deal of public attention. Every day, deep learning algorithms are used broadly across different industries. Deep learning has provided a revolutionary step to actualize AI. However, deep learning is still under active research and is considered complex and difficult.
Starting with an introduction to basic machine learning algorithms (related to deep learning), this book will help you understand the core concepts and mathematics of deep learning. We will quickly move on to explore neural networks and identify how to tackle challenges in larger networks using advanced algorithms. We will learn about the DL4J library and apply deep learning to various real-world use cases. Taking a hands-on practical approach, we will solve challenging problems in image processing, speech recognition, language modeling, and a wide variety of scenarios.
By the end of the book, we will have worked through practical examples following the best practices in Java for deep learning. As bonus content, we will discuss and explore other deep learning areas such as Theano and Caffe.
Yusuke Sugomori is a creative technologist with a background in information engineering. When he was a graduate school student, he cofounded Gunosy with his colleagues, which uses machine learning and web-based data mining to determine individual users' respective interests and provides an optimized selection of daily news items based on those interests. This algorithm-based app has gained a lot of attention since its release and now has more than 10 million users. The company has been listed on the Tokyo Stock Exchange since April 28, 2015. In 2013, Sugomori joined Dentsu, the largest advertising company in Japan based on nonconsolidated gross profit in 2014, where he carried out a wide variety of digital advertising, smartphone app development, and big data analysis. He was also featured as one of eight ""new generation"" creators by the Japanese magazine Web Designing. In April 2016, he joined a medical start-up as cofounder and CTO.
