WorldCat Identities

Puertas, Eloi

Overview
Works: 27 works in 45 publications in 5 languages and 122 library holdings
Roles: Contributor, Author
Publication Timeline
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Most widely held works by Eloi Puertas
Introduction to data science : a Python approach to concepts, techniques and applications by Laura Igual( Book )

6 editions published in 2017 in English and German and held by 88 WorldCat member libraries worldwide

This accessible and classroom-tested textbook/reference presents an introduction to the fundamentals of the emerging and interdisciplinary field of data science. The coverage spans key concepts adopted from statistics and machine learning, useful techniques for graph analysis and parallel programming, and the practical application of data science for such tasks as building recommender systems or performing sentiment analysis. Topics and features: provides numerous practical case studies using real-world data throughout the book; supports understanding through hands-on experience of solving data science problems using Python; describes techniques and tools for statistical analysis, machine learning, graph analysis, and parallel programming; reviews a range of applications of data science, including recommender systems and sentiment analysis of text data; provides supplementary code resources and data at an associated website
Extensions del programa Geogebra per a visualitzar conceptes matemàtics by Victor Franco Espino( )

2 editions published in 2007 in Catalan and held by 2 WorldCat member libraries worldwide

Análisis e implementación de un plugin de Eclipse para lenguajes funcionales : caso de ejemplo Lisp by Juan Gabriel Zazo Sancho( )

2 editions published in 2007 in Spanish and held by 2 WorldCat member libraries worldwide

The power of data : from the big data to deep learning( Book )

2 editions published in 2017 in Spanish and English and held by 2 WorldCat member libraries worldwide

Anàlisi i disseny d'una plataforma de robots Lego mindstorms per a formació d'equips by Albert Rosado Corrius( )

2 editions published in 2008 in Catalan and held by 2 WorldCat member libraries worldwide

Il potere dei dati : dal big data al deep learning( Book )

2 editions published between 2017 and 2019 in Italian and held by 2 WorldCat member libraries worldwide

El poder de los datos : del "big data" al aprendizaje profundo( Book )

2 editions published in 2017 in Spanish and held by 2 WorldCat member libraries worldwide

Adequació de l'aplicació de gestió d'horaris de la Facultat de Matemàtiques by Gerard Rojas i Bautista( )

2 editions published in 2006 in Catalan and held by 2 WorldCat member libraries worldwide

Implementació i simulació d'un sistema domòtic utilitzant el protocol x10 by Mariano Adolfo Ravinale( )

3 editions published between 2008 and 2009 in Spanish and Catalan and held by 2 WorldCat member libraries worldwide

Implementació d'un servidor en java per al joc Diplomacy by Jorge Rico Garriga( )

1 edition published in 2009 in Catalan and held by 1 WorldCat member library worldwide

Taller de robots mindstorms e inteligencia artificial para estudiantes de secundaria by David Trigo Chávez( )

1 edition published in 2010 in Spanish and held by 1 WorldCat member library worldwide

Aplicación estadística para eventos deportivos usando dispositivos móviles by Aurora Bernabé Quesada( )

1 edition published in 2009 in Catalan and held by 1 WorldCat member library worldwide

Taller de robots mindstorms e inteligencia artifical [sic] para estudiantes de secundaria : edición 2011 by Matilde Lucia Gallardo Pérez( )

2 editions published in 2011 in Spanish and held by 1 WorldCat member library worldwide

Implementación de un software para la captura y transmisión de imágenes mediante un dispositivo compacto by Sara Espinosa Hernández( )

1 edition published in 2009 in Spanish and held by 1 WorldCat member library worldwide

Multi-agent learning systems : a communicative approach by Eloi Puertas i Prats( )

1 edition published in 2007 in English and held by 1 WorldCat member library worldwide

Tutor Geogebra by Albert Vallès i Giménez( )

1 edition published in 2010 in Catalan and held by 1 WorldCat member library worldwide

Generalized stacked sequential learning by Eloi Puertas i Prats( Book )

2 editions published between 2014 and 2015 in English and held by 1 WorldCat member library worldwide

Over the past few decades, machine learning (ML) algorithms have become a very useful tool in tasks where designing and programming explicit, rule-based algorithms are infeasible. Some examples of applications where machine learning has been applied successfully are spam filtering, optical character recognition (OCR), search engines and computer vision. One of the most common tasks in ML is supervised learning, where the goal is to learn a general model able to predict the correct label of unseen examples from a set of known labeled input data. In supervised learning often it is assumed that data is independent and identically distributed (i.i.d). This means that each sample in the data set has the same probability distribution as the others and all are mutually independent. However, classification problems in real world databases can break this i.i.d. assumption. For example, consider the case of object recognition in image understanding. In this case, if one pixel belongs to a certain object category, it is very likely that neighboring pixels also belong to the same object, with the exception of the borders. Another example is the case of a laughter detection application from voice records. A laugh has a clear pattern alternating voice and non-voice segments. Thus, discriminant information comes from the alternating pattern, and not just by the samples on their own. Another example can be found in the case of signature section recognition in an e-mail. In this case, the signature is usually found at the end of the mail, thus important discriminant information is found in the context. Another case is part-of-speech tagging in which each example describes a word that is categorized as noun, verb, adjective, etc. In this case it is very unlikely that patterns such as [verb, verb, adjective, verb] occur. All these applications present a common feature: the sequence/context of the labels matters. Sequential learning (25) breaks the i.i.d. assumption and assumes that samples are not independently drawn from a joint distribution of the data samples X and their labels Y . In sequential learning the training data actually consists of sequences of pairs (x, y), so that neighboring examples exhibit some kind of correlation. Usually sequential learning applications consider one-dimensional relationship support, but these types of relationships appear very frequently in other domains, such as images, or video. Sequential learning should not be confused with time series prediction. The main difference between both problems lays in the fact that sequential learning has access to the whole data set before any prediction is made and the full set of labels is to be provided at the same time. On the other hand, time series prediction has access to real labels up to the current time t and the goal is to predict the label at t + 1. Another related but different problem is sequence classification. In this case, the problem is to predict a single label for an input sequence. If we consider the image domain, the sequential learning goal is to classify the pixels of the image taking into account their context, while sequence classification is equivalent to classify one full image as one class. Sequential learning has been addressed from different perspectives: from the point of view of meta-learning by means of sliding window techniques, recurrent sliding windows or stacked sequential learning where the method is formulated as a combination of classifiers; or from the point of view of graphical models, using for example Hidden Markov Models or Conditional Random Fields. In this thesis, we are concerned with meta-learning strategies. Cohen et al. (17) showed that stacked sequential learning (SSL from now on) performed better than CRF and H03 on a subset of problems called "sequential partitioning problems". These problems are characterized by long runs of identical labels. Moreover, SSL is computationally very efficient since it only needs to train two classifiers a constant number of times. Considering these benefits, we decided to explore in depth sequential learning using SSL and generalize the Cohen architecture to deal with a wider variety of problems
Software de pilotatge autònom per a plataforma robòtica AR. Drone by Carles R Riera Molina( )

1 edition published in 2012 in Catalan and held by 1 WorldCat member library worldwide

Manager online de fútbol by Víctor Cano Carmona( )

2 editions published in 2011 in Spanish and held by 1 WorldCat member library worldwide

Arquitectura d'Elearning per a Geogebra Tutor by Jordi Pascual Milego( )

1 edition published in 2010 in Catalan and held by 1 WorldCat member library worldwide

 
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Introduction to data science : a Python approach to concepts, techniques and applications
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Alternative Names
Puertas, Eloi

Puertas, Eloi (Puertas i Prats)

Puertas Prats, Eloi'

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