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• 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

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

• Solid & theoretical understanding of linear algebra, statistics & probability for predictive modeling
• Develop predictive models using classification, regression & clustering algorithms
• Develop predictive models for NLP
• Reinforcement learning for predictive analytics
• Factorization Machines for advanced recommendation systems
• Hands-on understanding of deep learning architectures for advanced predictive analytics
• Deep Neural Networks for predictive analytics
• Recurrent Neural Networks for predictive analytics
• Convolutional Neural Networks for emotion recognition, image classification & sentiment analysis.

Predictive analytics allows discovering hidden patterns from structured & unstructured data for automated decision making in business intelligence.

This book will help you build, tune & deploy predictive models with TensorFlow in three main sections. The first section covers linear algebra, statistics & probability theory for predictive modeling.

The second section shows developing predictive models via supervised (classification, regression) & unsupervised (clustering) algorithms. It then exhibits developing predictive models for NLP and covers reinforcement learning algorithms. Lastly, developing a Factorization Machines-based recommendation system is shown.

The third section covers deep learning architectures for advanced predictive analytics: including, Deep Neural Networks & Recurrent Neural Networks for high-dimensional and sequence data. Finally, Convolutional Neural Networks is used for predictive modeling for emotion recognition, image classification & sentiment analysis.