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Remarkable progress has been made in image recognition, primarily due to the availability of large-scale annotated datasets and deep convolutional neural networks (CNNs). CNNs enable learning data-driven, highly represen...

Subjects / keywords: Artificial intelligence; Deep learning; Computer science; Artificial neural network; Backpropagation; Reinforcement learning; Machine learning; Deep neural networks; Unsupervised learning; Recurrent neural network; Encod...

En este libro se introducen los conceptos fundamentales del aprendizaje profundo (Deep learning, DL) mediante el uso de redes neuronales artificiales (Artificial neural networks, ANN). El lector podrá encontrar una revi...

Automático (Machine Learning). El objetivo del machine learning es que los sistemas informáticos sean capaces de aprender a partir de los datos, emulando de esta manera las capacidades humanas. El Aprendizaje Profundo ...

La inteligencia artificial permite la innovación y el cambio en todos los aspectos de la vida moderna. La mayoría de los avances actuales se basan en Deep Learning, un área de conocimiento muy madura que permite a las...

Research in English Language Teaching in Mexico has recently increased due to the new demanads imposed by international agencies for researchers to publish. As a result, networks and collaboration among institutions have...

An end-to-end methodology based on deep learning for the detection and localization of microcalcifications in digital mammograms introduces a novel methodology designed for the preprocessing and localization of clusters ...

Lo primero que hicimos para iniciar la redacción de este libro fue realizar distintos ejercicios de autorreconocimento de nuestras propias funciones. Para ello, nos apoyamos en la matriz A center for teaching and learni...

The communicators’ training is one the fields with more updates due to constant changes faced every year. Therefore, digital native students have competencies developed in an informal way that become a natural bridge t...

We are pleased to present to the entire academic community and the general public the following work, which contains recent research results of a group of teachers from the Faculty of Educational Sciences in areas such a...

It is a well-known fact, accepted by most psychologists, that students assimilate knowledge in different ways. Several categories of learners have been identified: oral, visual, kinetic. This has doubtless implications f...

The network society (Van Dijk, 2020) has completely blurred the traditional difference between virtual and real life, Our day to day is increasingly characterised by our continuous connection to the Internet, Multiple da...

Long short-term memory (LSTM; Hochreiter & Schmidhuber, 1997) can solve numerous tasks not solvable by previous learning algorithms for recurrent neural networks (RNNs). We identify a weakness of LSTM networks processing...

With the continuous expansion of data availability in many large-scale, complex, and networked systems, such as surveillance, security, Internet, and finance, it becomes critical to advance the fundamental understanding ...

A comprehensive introduction to Support Vector Machines and related kernel methods. In the 1990s, a new type of learning algorithm was developed, based on results from statistical learning theory: the Support Vector Mach...

Deep learning has revolutionized many machine learning tasks in recent years, ranging from image classification and video processing to speech recognition and natural language understanding. The data in these tasks are t...

Recurrent neural networks (RNNs) have been widely adopted in research areas concerned with sequential data, such as text, audio, and video. However, RNNs consisting of sigma cells or tanh cells are unable to learn the re...

Deep convolutional neural networks have performed remarkably well on many Computer Vision tasks. However, these networks are heavily reliant on big data to avoid overfitting. Overfitting refers to the phenomenon when a n...

The popular Q-learning algorithm is known to overestimate action values under certain conditions. It was not previously known whether, in practice, such overestimations are common, whether they harm performance, and whet...

Importance: Deep learning is a family of computational methods that allow an algorithm to program itself by learning from a large set of examples that demonstrate the desired behavior, removing the need to specify rules ...

Due to the simplicity of their implementations, least square support vector machine (LS-SVM) and proximal support vector machine (PSVM) have been widely used in binary classification applications. The conventional LS-SVM...

Multilayer neural networks trained with the back-propagation algorithm constitute the best example of a successful gradient based learning technique. Given an appropriate network architecture, gradient-based learning alg...

Lots of learning tasks require dealing with graph data which contains rich relation information among elements. Modeling physics systems, learning molecular fingerprints, predicting protein interface, and classifying dis...

We propose a deep learning method for single image super-resolution (SR). Our method directly learns an end-to-end mapping between the low/high-resolution images. The mapping is represented as a deep convolutional neural...