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Discover expert techniques for combining machine learning with the analytic capabilities of Elastic Stack and uncover actionable insights;
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with your Elastic Stack to improve its performance Book DescriptionMachine Learning with the Elastic Stack is a comprehensive overview of the;
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Use the Elastic Stack for search, security, and observability-related use cases while working with large amounts of data on-premise and on;
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deploy Elastic Stack in production environments. By the end of this book, you'll be well versed with the fundamental Elastic Stack;
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to grips with the new features introduced in Elastic Stack 6.0 * Get valuable insights from your data by working with the different;
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who are familiar with the basics of Elasticsearch, Logstash, and Kibana, and now want to become an expert at using the Elastic stack for data;
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Learn how to install, configure and implement the Elastic Stack (Elasticsearch, Logstash and Kibana) - the invaluable tool for anyone;
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Create and deploy full-stack Spring Boot applications with React and AWS. This practical and authoritative guide teaches you the;
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-driven architecture and the Elastic stack * Build effective dashboards for data visualization and explore datasets with Elastic Graph * A;
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apply the machine learning stack within R, which includes using various R packages such as glmnet, h2o, ranger, xgboost, keras, and others to;
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Amazon Web Services. The Amazon AI and machine learning stack unifies data science, data engineering, and application development to help level;
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navigating the Elastic stack Work with different Kibana plugins and create effective machine learning jobs using Kibana Build effective dashboards;
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generative adversarial networks using Python libraries Work on threat hunting with machine learning and the ELK stack Explore the best practices for;
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of all sorts. Deep Learning with Structured Data shows you how to apply powerful deep learning analysis techniques to the kind;
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create automated machine learning modules and helps you get up to speed with them in the most practical way possible. In this book, you'll learn;
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frictionless (require minimal to no human intervention) and elastic (machine learning solutions that can scale up or down by controlling the number;
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Build machine and deep learning systems with the newly released TensorFlow 2 and Keras for the lab, production, and mobile devices Key;
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learning applications using Spark MLlib and how to manage the machine learning development lifecycle. This book is packed with practical examples;
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error evaluation process by pinpointing parameters (e.g., architectural) with the most substantial impact on the software stack dependability;
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error evaluation process by pinpointing parameters (e.g., architectural) with the most substantial impact on the software stack dependability;
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With a new generation of services and frameworks, frontend and mobile developers can use their existing skill set to build full stack;
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This book studies mathematical theories of machine learning. The first part of the book explores the optimality and adaptivity of choosing;
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Prepare to achieve AWS Machine Learning Specialty certification with this complete, up-to-date guide and take the exam with confidence;
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Work smarter with machine learning! Begin with core machine learning concepts-types of learning, algorithms, data preparation, and more;
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Master the essential skills needed to recognize and solve complex problems with machine learning and deep learning. Using real-world;
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"Explore machine learning in Rust and learn about the intricacies of creating machine learning applications"--;
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Navigate the world of data analysis, visualization, and machine learning with over 100 hands-on Scala recipes About This Book;
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