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The 'Pathways' transformative knowledge network is an international group of research organisations, collaborating to explore processes of social transformation and to share insights across disciplines, cultures and cont...

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...

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...

This Campbell systematic review assesses the effectiveness of parental, familial, and community support for children's literacy development in developing countries. The review summarises findings from 13 studies, of whic...

We present a highly accurate single-image superresolution (SR) method. Our method uses a very deep convolutional network inspired by VGG-net used for ImageNet classification [19]. We find increasing our network depth sho...

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...

Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers...

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 ...

TensorFlow is a machine learning system that operates at large scale and in heterogeneous environments. Tensor-Flow uses dataflow graphs to represent computation, shared state, and the operations that mutate that state. ...

Several variants of the long short-term memory (LSTM) architecture for recurrent neural networks have been proposed since its inception in 1995. In recent years, these networks have become the state-of-the-art models for...

Prediction tasks over nodes and edges in networks require careful effort in engineering features used by learning algorithms. Recent research in the broader field of representation learning has led to significant progres...

Subjects / keywords: Machine learning; Neural networks (Computer science)

Subjects / keywords: Computer-assisted instruction; Learning; Technological innovations; Teaching; Computer network resources

Subjects / keywords: Neural networks (Computer science); Artificial intelligence; Information networks; Machine learning

In this work, we revisit the global average pooling layer proposed in [13], and shed light on how it explicitly enables the convolutional neural network (CNN) to have remarkable localization ability despite being trained...