Universal Design Principles and Models


Universal Design Principles and Models
Universal Design: Principles and Models By Roberta Null
2013 | 394 Pages | ISBN: 146650529X | PDF | 125 MB

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CGAxis Models Volume 68 Furniture V

CGAxis Models Volume 68 Furniture V
CGAxis Models Volume 68 Furniture V
max | c4d | obj | fbx | 3D Models | 1.89 GB

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The Decision Book Fifty Models for Strategic Thinking


The Decision Book Fifty Models for Strategic Thinking

The Decision Book: Fifty Models for Strategic Thinking by Mikael Krogerus
English | ISBN 10: 0393079619 | 2012 | MOBI | 176 pages | 1,5 MB

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Implementing Data Models and Reports

Implementing Data Models and Reports
Implementing Data Models and Reports
MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 4.5 Hours | 1.58 GB
Genre: eLearning | Language: English

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Coursera Stanford University - Probabilistic Graphical Models (2018)

Coursera Stanford University - Probabilistic Graphical Models  (2018)
Coursera: Stanford University - Probabilistic Graphical Models
WEBRip | English | MP4 + PDF Slides | 960 x 540 | AVC ~39.6 kbps | 15 fps
AAC | 128 Kbps | 44.1 KHz | 2 channels | Subs: English (.srt) | 23:25:47 | 1.36 GB
Genre: eLearning Video / Computer Science, Engineering and Technology

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Industrial Mathematics and Complex Systems Emerging Mathematical Models, Methods and Algorithms


Industrial Mathematics and Complex Systems Emerging Mathematical Models, Methods and Algorithms

Industrial Mathematics and Complex Systems: Emerging Mathematical Models, Methods and Algorithms By Pammy Manchanda, Rene Lozi, Abul Hasan Siddiqi
English | PDF,EPUB | 2017 | 358 Pages | ISBN : 9811037574 | 21.72 MB
The book discusses essential topics in industrial and applied mathematics such as image processing with a special focus on medical imaging, biometrics and tomography. Applications of mathematical concepts to areas like national security, homeland security and law enforcement, enterprise and e-government services, personal information and business transactions, and brain-like computers are also highlighted.

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Improving Flood Prediction Assimilating Uncertain Crowdsourced Data into Hydrologic and Hydraulic Models


Improving Flood Prediction Assimilating Uncertain Crowdsourced Data into Hydrologic and Hydraulic Models

Improving Flood Prediction Assimilating Uncertain Crowdsourced Data into Hydrologic and Hydraulic Models by Maurizio Mazzoleni
English | 2017 | ISBN: 1138035904 | 240 pages | PDF | 47,7 MB

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O'Reilly - Monitoring and Improving the Performance of Machine Learning Models

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O'Reilly - Monitoring and Improving the Performance of Machine Learning Models
English | Size: 863.79 MB
Category: CBTs

It’s critical to have “humans in the loop” when automating the deployment of machine learning (ML) models. Why? Because models often perform worse over time.

This course covers the human directed safeguards that prevent poorly performing models from deploying into production and the techniques for evaluating models over time. We’ll use ModelDB to capture the appropriate metrics that help you identify poorly performing models. We'll review the many factors that affect model performance (i.e., changing users and user preferences, stale data, etc.) and the variables that lose predictive power. We'll explain how to utilize classification and prediction scoring methods such as precision recall, ROC, and jaccard similarity.
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O'Reilly - Deploying Machine Learning Models as Microservices Using Docker

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O'Reilly - Deploying Machine Learning Models as Microservices Using Docker
English | Size: 825.91 MB
Category: CBTs

Modern applications running in the cloud often rely on REST-based microservices architectures by using Docker containers. Docker enables your applications to communicate between one another and to compose and scale various components. Data scientists use these techniques to efficiently scale their machine learning models to production applications.
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O'Reilly - An Introduction to Machine Learning Models in Production

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O'Reilly - An Introduction to Machine Learning Models in Production
English | Size: 1.38 GB
Category: CBTs

This course lays out the common architecture, infrastructure, and theoretical considerations for managing an enterprise machine learning (ML) model pipeline. Because automation is the key to effective operations, you'll learn about open source tools like Spark, Hive, ModelDB, and Docker and how they're used to bridge the gap between individual models and a reproducible pipeline.

You'll also learn how effective data teams operate; why they use a common process for building, training, deploying, and maintaining ML models; and how they're able to seamlessly push models into production.
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