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Jblearning Paramedic

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12 documents available in our comprehensive collection of Jblearning Paramedic resources. Find practical guides, tutorials, and documentation to enhance your knowledge.

Preview of 2023 Handout (PDF - 55 Pages)
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2023 Handout (PDF - 55 Pages) (Aubin_HAND_2023_AU_Curves.pdf)

3.22 MBPatricia LundbergEN 55 pages
The session "Taming Parametric Curved Geometry in the Revit Family Editor" by Paul F.
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Slides (Pricing_Slides.pdf)

600 KBEN 15 pages
Semi-parametric dynamic contextual pricing is discussed, where e-commerce platforms use data on user history and product types to optimize revenue through...
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Preview of Probabilistic Multimodal Modeling for Human-Robot Interaction Tasks
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Probabilistic Multimodal Modeling for Human-Robot Interaction Tasks (p47.pdf)

4.51 MBEN 9 pages
Probabilistic Multimodal Modeling for Human-Robot Interaction Tasks: This work introduces a reformulation of Interaction Primitives, called ensemble Bayesian...
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presentation (Class_Presentation_CES321585_Jim_Crabtree.pdf)

2.27 MBJanice Miller-KellermanEN 18 pages
Bridge Detailing 2.0 covers computational modelling methods using Civil 3D, Revit, and Dynamo, presented by Jim Crabtree, a BIM-certified bridge design...
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pdf (P13-1148.pdf)

384 KBBenjamin Borschinger ; Mark Johnson ; Katherine DemuthEN 9 pages
Researchers proposed a joint model of word segmentation and phonological variation for English word-final /t/-deletion, extending a non-parametric model of...
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Preview of Machine Learning
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Machine Learning (regressionnew.pdf)

6.38 MBEN 50 pages
Machine Learning categories include Unsupervised, Supervised, and Reinforcement Learning. These involve clustering, density estimation, and discriminative or generative models. Learning occurs from a training data set.
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Download PDF (625b21662e99886bf6004023_UseCase_TeamCollaboration_v2.pdf)

1.94 MBEN 4 pages
The City of Quebec developed a Virtual Reality training program for public safety and security professionals, including police officers, firefighters, and...
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Preview of Machine Learning
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Machine Learning (L6-KNN.pdf)

947 KBEN 23 pages
Machine learning and data mining involve supervised and unsupervised learning. Supervised learning includes classification problems, where a function is learned from a training set. K-nearest neighbors (KNN) is a simple method used for classification.
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Preview of Vincent, P. (2011). A connection between score matching and denoising autoencoders. Neural Computation, 23(7).
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Vincent, P. (2011). A connection between score matching and denoising autoencoders. Neural Computation, 23(7). (smdae_techreport_1358.pdf)

274 KBEN 13 pages
Denoising autoencoders are equivalent to a form of regularized score matching, which is a technique for learning parameters of unnormalized density models.
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Preview of School of Health Professions Student Orientation Handbook 2023
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School of Health Professions Student Orientation Handbook 2023 (2023 Certificate Handbook.pdf)

1.88 MBManiaci, JoanmarieEN 62 pages
A comprehensive guide for students in the School of Health Professions, covering academic policies, procedures, and regulations, including mission statement, student responsibilities, diversity commitments, and certificate program details.
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Preview of Data-driven modelling: an integrative approach for various application areas
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Data-driven modelling: an integrative approach for various application areas (program.pdf)

14 KBAnna MarconatoEN 3 pages
Prof. Johan Suykens presents an integrative approach to data-driven modelling, focusing on advanced black-box techniques for diverse applications. The lecture covers challenges and future directions in high-dimensional data analysis, with a specific emphasis on Support Vector...
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Preview of Optimizing Functionals on the Space of Probabilities with Input Convex Neural Networks
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Optimizing Functionals on the Space of Probabilities with Input Convex Neural Networks (2106.00774.pdf)

5.64 MBEN 32 pages
Gradient flows are used to optimize functionals in probability spaces with the Wasserstein metric. A typical approach involves the Jordan-Kinderlehrer-Otto scheme, but this is challenging in high dimensions. We propose using input-convex neural networks to approximate the scheme.
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