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 Enterprise Content and Search Management for Building Digital Platforms
Provides modern enterprises with the tools to create a robust digital platform utilizing proven best practices, practical models, and time-tested techniques
Contemporary business organizations can either embrace the digital revolution—or be left behind. Enterprise Content and Search Management for Building Digital... |  |  Deep Learning with Python: A Hands-on Introduction
Discover the practical aspects of implementing deep-learning solutions using the rich Python ecosystem. This book bridges the gap between the academic state-of-the-art and the industry state-of-the-practice by introducing you to deep learning frameworks such as Keras, Theano, and Caffe. The practicalities of these frameworks is often ... |  |  Microservices: Flexible Software Architecture
The Most Complete, Practical, and Actionable Guide to Microservices
Going beyond mere theory and marketing hype, Eberhard Wolff presents all the knowledge you need to capture the full benefits of this emerging paradigm. He illuminates... |
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 TypeScript Design Patterns
Key Features
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This step-by-step guide will would demonstrate all the important design patterns in practice
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This book is the only documentation on the market focusing on design patterns in TypeScript
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This book is packed with rich examples that will improve...
|  |  Algebraic Shift Register Sequences
Pseudo-random sequences are essential ingredients of every modern digital communication system including cellular telephones, GPS, secure internet transactions and satellite imagery. Each application requires pseudo-random sequences with specific statistical properties. This book describes the design, mathematical analysis and implementation... |  |  Big Data SMACK: A Guide to Apache Spark, Mesos, Akka, Cassandra, and Kafka
Learn how to integrate full-stack open source big data architecture and to choose the correct technologyâScala/Spark, Mesos, Akka, Cassandra, and Kafkaâin every layer.
Big data architecture is becoming a requirement for many different enterprises. So far, however, the focus has largely been on collecting,... |
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 R Deep Learning Essentials
Key Features
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Harness the ability to build algorithms for unsupervised data using deep learning concepts with R
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Master the common problems faced such as overfitting of data, anomalous datasets, image recognition, and performance tuning while building the models
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Build models relating to neural
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