Deep learning is a powerful tool for extracting insight from structured data, such as relational databases, and can augment existing machine learning and business intelligence systems. This book teaches how to apply deep learning analysis techniques to structured data and provides practical, relevant applications for readers with intermediate knowledge of Python, Jupyter notebooks, and machine learning. Format: Paperback / softback Length: 264 pages Publication date: 23 February 2021 Publisher: Manning Publications Most businesses prioritize accurate forecasting and fraud detection using their structured datasets, rather than focusing on identifying cats in YouTube videos. The power of deep learning techniques lies in their ability to efficiently extract valuable insights from the structured data commonly collected by organizations. Deep learning possesses the capability to identify intricate patterns and relationships within data of various types."Deep Learning with Structured Data" is a comprehensive guide that demonstrates how to leverage powerful deep learning analysis techniques on the structured, tabular data found in relational databases, which are crucial for real-world businesses. This book is packed with practical and relevant applications, teaching readers how deep learning can enhance their existing machine learning and business intelligence systems.In this book, you will explore the benefits and limitations of deep learning, learn how to organize data for your deep learning models, understand the deep learning stack, and measure the performance of your models. It is designed for readers with an intermediate knowledge of Python, Jupyter notebooks, and machine learning.By leveraging the power of deep learning with structured data, businesses can gain a competitive edge by making informed decisions based on accurate predictions and detecting fraudulent activities. This book provides the necessary knowledge and tools to unlock the full potential of deep learning in the business world. Weight: 510g Dimension: 844 x 235 x 17 (mm) ISBN-13: 9781617296727
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