Java Deep Learning Essentials 1st Edition Sugomori Yusuke by Sugomori, Yusuke 9781785282195, 1785282190 instant download after payment.

Solve complex data science tasks through practical applications of deep learning with JavaAbout 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 neededWho This Book Is ForThis 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 CaffeIn DetailWith 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.