Predictive Analytics with TensorFlow

More Information
  • 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

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.

  • 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
Page Count 522
Course Length 15 hours 39 minutes
ISBN 9781788398923
Date Of Publication 2 Nov 2017


Md. Rezaul Karim

Md. Rezaul Karim is a researcher, author, and data science enthusiast with a strong computer science background, coupled with 10 years of research and development experience in machine learning, deep learning, and data mining algorithms to solve emerging bioinformatics research problems by making them explainable. He is passionate about applied machine learning, knowledge graphs, and explainable artificial intelligence (XAI). Currently, he is working as a research scientist at Fraunhofer FIT, Germany. He is also a PhD candidate at RWTH Aachen University, Germany. Before joining FIT, he worked as a researcher at the Insight Centre for Data Analytics, Ireland. Previously, he worked as a lead software engineer at Samsung Electronics, Korea.