Accomplish the power of data in your business by building advanced predictive modelling applications with Tensorflow. About This Book * A quick guide to gain hands-on experience with deep learning in different domains such as digit/image classification, and texts * Build your own smart, predictive models with TensorFlow using easy-to-follow approach mentioned in the book * Understand deep learning and predictive analytics along with its challenges and best practices Who This Book Is For This book is intended for anyone who wants to build predictive models with the power of TensorFlow from scratch. If you want to build your own extensive applications which work, and can predict smart decisions in the future then this book is what you need! What You Will Learn * Get a solid and theoretical understanding of linear algebra, statistics, and probability for predictive modeling * Develop predictive models using classification, regression, and clustering algorithms * Develop predictive models for NLP * Learn how to use reinforcement learning for predictive analytics * Factorization Machines for advanced recommendation systems * Get a hands-on understanding of deep learning architectures for advanced predictive analytics * Learn how to use deep Neural Networks for predictive analytics * See how to use recurrent Neural Networks for predictive analytics * Convolutional Neural Networks for emotion recognition, image classification, and sentiment analysis In Detail Predictive analytics discovers hidden patterns from structured and unstructured data for automated decision-making in business intelligence. This book will help you build, tune, and deploy predictive models with TensorFlow in three main sections. The first section covers linear algebra, statistics, and probability theory for predictive modeling. The second section covers developing predictive models via supervised (classification and regression) and unsupervised (clustering) algorithms. It then explains how to develop predictive models for NLP and covers reinforcement learning algorithms. Lastly, this section covers developing a factorization machines-based recommendation system. The third section covers deep learning architectures for advanced predictive analytics, including deep neural networks and recurrent neural networks for high-dimensional and sequence data. Finally, convolutional neural networks are used for predictive modeling for emotion recognition, image classification, and sentiment analysis. Style and approach TensorFlow, a popular library for machine learning, embraces the innovation and community-engagement of open source, but has the support, guidance, and stability of a large corporation.

Author Biography

Md. Rezaul Karim is a Research Scientist at Fraunhofer FIT, Germany. He is also a PhD candidate at RWTH Aachen University, Aachen, Germany. He holds a BSc and an MSc degree in Computer Science. Before joining Fraunhofer FIT, he worked as a Researcher at Insight Centre for Data Analytics, Ireland. Before this, he worked as a Lead Engineer at Samsung Electronics' distributed R&D Institutes in Korea, India, Turkey, and Bangladesh. Previously, he has worked as a Research Assistant at the database lab, Kyung Hee University, Korea. He also worked as an R&D engineer with BMTech21 Worldwide, Korea. Even before this, he worked as a Software Engineer with i2SoftTechnology, Dhaka, Bangladesh. He has more than 8 years of experience in the area of research and development with solid understanding of algorithms and data structures in C, C++, Java, Scala, R, and Python. He has published several books, articles, and research papers concerning big data and virtualization technologies, such as Spark, Kafka, DC/OS, Docker, Mesos, Zeppelin, Hadoop, and MapReduce. He is also equally competent with deep learning technologies such as TensorFlow, DeepLearning4j, and H2O. His research interests include Machine Learning, Deep Learning, Semantic Web, Linked Data, Big Data, and Bioinformatics. Also, he is the author of the following book titles: * Large-Scale Machine Learning with Spark (Packt Publishing Ltd.) * Deep Learning with TensorFlow (Packt Publishing Ltd.) * Scala and Spark for Big Data Analytics (Packt Publishing Ltd.)