Las fracciones en la vida diariaamoaljuano86El documento describe cómo las fracciones están presentes en la vida cotidiana, como cuando hacemos compras, comemos con amigos o comemos postre, pero a pesar de su uso común, dentro del aula escolar las fracciones a menudo parecen desconocidas y difíciles. El autor argumenta que debemos enseñar las fracciones utilizando ejemplos de la vida diaria para lograr una comprensión más significativa.
Nonlinear component analysis as a kernel eigenvalue problemMichele FilanninoThis presentation summarizes paper #7 titled "Nonlinear component analysis as a kernel eigenvalue problem" by Scholkopf, Smola, and Muller. It introduces Kernel Principal Component Analysis (KPCA) as an extension of PCA that maps data into a higher dimensional feature space. The presentation discusses how KPCA frames PCA as a kernel eigenvalue problem and computes principal components in this new feature space. It provides the mathematical formulation and algorithm for KPCA. The presentation also discusses applications, advantages, disadvantages, and experiments comparing KPCA to other dimensionality reduction techniques.
G-TAD: Sub-Graph Localization for Temporal Action DetectionMengmeng XuThe document proposes G-TAD, a method that casts temporal action detection as a sub-graph localization problem in a graph representation of a video. G-TAD uses GCNeXt blocks to learn features by aggregating temporal and semantic context, and SGAlign layers to embed sub-graphs into a fixed-size representation. Experimental results show G-TAD achieves state-of-the-art performance on temporal action detection benchmarks.
Generative Adversarial Networks (GANs)Luis SerranoThis document provides a friendly introduction to generative adversarial networks (GANs). It explains the general idea of GANs which involve a discriminator and generator playing a game, with the goal of the generator being to generate fake images that cannot be distinguished from real images by the discriminator. The document then walks through building the simplest GAN with a 1-layer neural network discriminator and generator. It explains how to train the GAN by having the discriminator and generator update through backpropagation to minimize their loss functions. Code examples are provided to demonstrate how to implement the GAN.
Sesión de aprendizajeAugustoEste documento trata sobre la semejanza de triángulos. Explica que dos triángulos son semejantes si cumplen una de tres condiciones: tener dos ángulos iguales, tener dos lados proporcionales e igual el ángulo que forman, o tener lados proporcionales. Presenta ejemplos de triángulos semejantes y ejercicios para determinar si triángulos dados son semejantes basado en la proporcionalidad de sus lados.
PCA Final.pptxHarisMasood20PCA is a technique used to reduce the dimensionality of large data sets by transforming the data to a new set of variables called principal components. It works by identifying the directions of maximum variance in high-dimensional data and projecting the data onto these directions while preserving as much information as possible. The principal components are the eigenvectors of the covariance matrix and represent the directions with maximum variability in the data. Dimensionality reduction is achieved by keeping only the first few principal components and ignoring the rest based on their eigenvalues.
Medical Ai perspectivesNamkug Kim2017년 6월 10일(토) 서울아산병원에서 개최되었던 '의료 딥러닝 워크샵-기초부터 진보까지' 교육워크샵 발표자료를 올려드립니다.
공개 불가능 자료는 일부 삭제되었으니 양해를 부탁드립니다.
*울산대학교 김남국교수 강연 자료
ʰDZ辱岹---ѳܱپó-貹-ձ-Ұ--ʰ.dzNEMECIO PICHUCAEl documento presenta información sobre las propiedades de la multiplicación: conmutativa, asociativa y distributiva. Incluye ejemplos y ejercicios para practicar cada propiedad en igualdades y problemas matemáticos. El documento concluye con más actividades de multiplicación para reforzar los conceptos aprendidos.
Dimension Reduction Introduction & PCA.pptxRohanBorgalliPCA is a dimensionality reduction technique that uses linear transformations to project high-dimensional data onto a lower-dimensional space while retaining as much information as possible. It works by identifying patterns in data and expressing the data in such a way as to highlight their similarities and differences. Specifically, PCA uses linear combinations of the original variables to extract the most important patterns from the data in the form of principal components. The first principal component accounts for as much of the variability in the data as possible, and each succeeding component accounts for as much of the remaining variability as possible.
Clase 3 (ángulos complementarios y suplementarios)Alan Valenzuela TapiaEl documento presenta una lección sobre ángulos complementarios y suplementarios. Explica la clasificación de ángulos y define ángulos opuestos por el vértice y ángulos adyacentes con ejemplos. Luego define ángulos complementarios como aquellos cuyas medidas suman 90 grados, e insta a los estudiantes a practicar en la página 46 de su cuaderno. Finalmente, define ángulos suplementarios como aquellos cuyas medidas suman 180 grados, e insta a los estudiantes a practicar en la página 47.
Unity Is Strength - Action plan template Texas Health Care AssociationThis document outlines a performance improvement plan template that uses the FOCUS methodology. The FOCUS methodology involves finding processes to improve, organizing a team, clarifying the process, understanding variation, and selecting variations to improve using small tests of changes. The template includes sections to identify areas for improvement, action plan steps, completion dates, and measurement strategies with responsibilities assigned.
Deep Learning for Video: Action Recognition (UPC 2018)Universitat Politècnica de Catalunyahttps://mcv-m6-video.github.io/deepvideo-2018/
Overview of deep learning solutions for video processing. Part of a series of slides covering topics like action recognition, action detection, object tracking, object detection, scene segmentation, language and learning from videos.
Prepared for the Master in Computer Vision Barcelona:
http://pagines.uab.cat/mcv/
Medical Ai perspectivesNamkug Kim2017년 6월 10일(토) 서울아산병원에서 개최되었던 '의료 딥러닝 워크샵-기초부터 진보까지' 교육워크샵 발표자료를 올려드립니다.
공개 불가능 자료는 일부 삭제되었으니 양해를 부탁드립니다.
*울산대학교 김남국교수 강연 자료
ʰDZ辱岹---ѳܱپó-貹-ձ-Ұ--ʰ.dzNEMECIO PICHUCAEl documento presenta información sobre las propiedades de la multiplicación: conmutativa, asociativa y distributiva. Incluye ejemplos y ejercicios para practicar cada propiedad en igualdades y problemas matemáticos. El documento concluye con más actividades de multiplicación para reforzar los conceptos aprendidos.
Dimension Reduction Introduction & PCA.pptxRohanBorgalliPCA is a dimensionality reduction technique that uses linear transformations to project high-dimensional data onto a lower-dimensional space while retaining as much information as possible. It works by identifying patterns in data and expressing the data in such a way as to highlight their similarities and differences. Specifically, PCA uses linear combinations of the original variables to extract the most important patterns from the data in the form of principal components. The first principal component accounts for as much of the variability in the data as possible, and each succeeding component accounts for as much of the remaining variability as possible.
Clase 3 (ángulos complementarios y suplementarios)Alan Valenzuela TapiaEl documento presenta una lección sobre ángulos complementarios y suplementarios. Explica la clasificación de ángulos y define ángulos opuestos por el vértice y ángulos adyacentes con ejemplos. Luego define ángulos complementarios como aquellos cuyas medidas suman 90 grados, e insta a los estudiantes a practicar en la página 46 de su cuaderno. Finalmente, define ángulos suplementarios como aquellos cuyas medidas suman 180 grados, e insta a los estudiantes a practicar en la página 47.
Unity Is Strength - Action plan template Texas Health Care AssociationThis document outlines a performance improvement plan template that uses the FOCUS methodology. The FOCUS methodology involves finding processes to improve, organizing a team, clarifying the process, understanding variation, and selecting variations to improve using small tests of changes. The template includes sections to identify areas for improvement, action plan steps, completion dates, and measurement strategies with responsibilities assigned.
Deep Learning for Video: Action Recognition (UPC 2018)Universitat Politècnica de Catalunyahttps://mcv-m6-video.github.io/deepvideo-2018/
Overview of deep learning solutions for video processing. Part of a series of slides covering topics like action recognition, action detection, object tracking, object detection, scene segmentation, language and learning from videos.
Prepared for the Master in Computer Vision Barcelona:
http://pagines.uab.cat/mcv/