Today’s Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach.The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms.
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How to install Sims 4 ModsOnly a few easy steps and you will know how to install Sims 4 Mods on your computer, keep reading! Open up the Downloads folder and then right-click on what you’ve just downloaded. If you’re using WinRar application, you should see an icon that looks like a stack of books. Once clicked, it will give you an option that says, “Extract to (mod name)”.
ObjectivesThis 5-day course helps data engineers focus on essential design and architecture while building a data lake and relevant processing platform.Participants will learn various aspects of data engineering while building resilient distributed datasets. Participants will learn to apply key practices, identify multiple data sources appraised against their business value, design the right storage, and implement proper access model(s). Finally, participants will build a scalable data pipeline solution composed of pluggable component architecture, based on the combination of requirements in a vendor/technology agnostic manner. Participants will familiarize themselves on working with Spark platform along with additional focus on query and streaming libraries.This course is part of the Analytics and Intelligent Systems series offered by NUS-ISS.Upon effective completion of the course, participants will be able to:- Understand the growth of big data and need for a scalable processing framework. Understand the fundamental characteristics, storage, analysis techniques and the relevant distributions;- Understand the distributed storage essentials, storage needs, and relevant architectural mechanism in processing large amounts of structured, semi-structured and unstructured data;- Gain expertise with the fault-tolerant computing framework (E.g. YARN) by setting up pseudo cluster nodes or cloud based nodes for processing big data;- Construct configurable and executable tasks using the In Memory Processing frameworks (E.g. Understand the nuances of writing functional programs and use the core libraries to manipulate the large corpse of unstructured data residing as Resilient Distributed Datasets;- Organize, store and manipulate the collected data using processing libraries.
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Minimum:. OS: WINDOWS® 7, 8.1, 10 (64-BIT Required). Processor: Intel® Core™ i5-4460 or AMD FX™-6300 or better. Memory: 8 GB RAM. Graphics: NVIDIA® GeForce® GTX 760 or AMD Radeon™ R7 260x with 2GB Video RAM. DirectX: Version 11.
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