Conclude AI chapter
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#+PANDOC_OPTIONS: csl:assets/ieee.csl
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#+PANDOC_OPTIONS: top-level-division:chapter
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* Resumen
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@ -114,13 +115,27 @@ Una neurona artificial es un modelo de una neurona biológica, cada neurona reci
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#+NAME: fig:artificial-neuron
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[[./assets/figures/artificial-neuron.png]]
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Una red neuronal artificial (ANN) es una red de capas de neuronas artificiales. Una ANN está formada por una capa de entrada, capas ocultas y una capa de salida. Las neuronas de una capa están conectadas, total o parcialmente, a las neuronas de la capa siguiente. También son posibles las conexiones de retroalimentación con las capas anteriores. La estructura típica de una ANN es la siguiente:
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El proceso de activación se puede expresar como un modelo matemático:
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\begin{equation}
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y= f \left(\sum\limits_{i=0}^{n} w_{i}x_{i} - T \right)
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\end{equation}
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donde $y$ es la salida del nodo, $f$ es la función de activación, $w_i$ es el peso de la entrada $x_{i}$ , y $T$ es el valor del umbral. cite:Zou2009
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Una red neuronal artificial (ANN) es una red de capas de neuronas artificiales. Una ANN está formada por una capa de entrada, capas ocultas y una capa de salida. Las neuronas de una capa están conectadas, total o parcialmente, a las neuronas de la capa siguiente. También son posibles las conexiones de retroalimentación con las capas anteriores. cite:book:80129 La estructura típica de una ANN es la siguiente:
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\clearpage
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#+CAPTION: Estructura de una red neuronal artificial cite:book:80129
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#+ATTR_HTML: :height 30% :width 50%
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#+NAME: fig:neural-network
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[[./assets/figures/neural-network.png]]
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Los principios básicos de las redes neuronales artificiales fueron formulados por primera vez en 1943, y el perceptrón, que históricamente es posiblemente la primera neurona artificial, se propuso en 1958. cite:book:2610592 Sin embargo, estos modelos no fueron populares hasta mediados de la década de 1980, cuando se reinventó el algoritmo de /back-propagation/. cite:book:771224
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En la actualidad, los avances tanto en potencia de cálculo del /hardware/, especialmente en las tarjetas gráficas (GPU) cite:Cireşan2010, como la disponibilidad de grandes /datasets/ cite:book:771224 han dado lugar al /Deep Learning/.
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** Hacia el Deep Learning
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* Estado del arte
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** Bioinformática
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Dissertation.pdf
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Dissertation.pdf
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@ -561,7 +561,7 @@
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year = 2010,
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series = {Prentice Hall Series in Artificial Intelligence},
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edition = {3rd},
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pages = {38-45, 55-56}
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pages = {38-45, 48-49, 55-56}
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}
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@article{McCarthy_Minsky_Rochester_Shannon_2006,
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@ -599,3 +599,66 @@
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edition = 2,
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pages = {39-40}
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}
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@Inbook{Zou2009,
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author = "Zou, Jinming and Han, Yi and So, Sung-Sau",
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editor = "Livingstone, David J.",
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title = "Overview of Artificial Neural Networks",
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bookTitle = "Artificial Neural Networks: Methods and Applications",
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year = 2009,
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publisher = "Humana Press",
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address = "Totowa, NJ",
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pages = "14--22",
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abstract = "The artificial neural network (ANN), or simply neural
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network, is a machine learning method evolved from the idea of
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simulating the human brain. The data explosion in modern drug
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discovery research requires sophisticated analysis methods to
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uncover the hidden causal relationships between single or
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multiple responses and a large set of properties. The ANN is
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one of many versatile tools to meet the demand in drug
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discovery modeling. Compared to a traditional regression
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approach, the ANN is capable of modeling complex nonlinear
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relationships. The ANN also has excellent fault tolerance and
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is fast and highly scalable with parallel processing. This
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chapter introduces the background of ANN development and
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outlines the basic concepts crucially important for
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understanding more sophisticated ANN. Several commonly used
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learning methods and network setups are discussed briefly at
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the end of the chapter.",
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isbn = "978-1-60327-101-1",
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doi = "10.1007/978-1-60327-101-1_2",
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url = "https://doi.org/10.1007/978-1-60327-101-1_2"
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}
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@book{book:2610592,
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title = {Principles of artificial neural networks},
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author = {Graupe, Daniel},
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publisher = {World Scientific Publ},
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isbn = {9789814522731,9814522732},
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year = 2013,
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edition = {3. ed},
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pages = {28-31}
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}
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@Article{Cireşan2010,
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author = {Cire{\c{s}}an, Dan Claudiu and Meier, Ueli and Gambardella,
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Luca Maria and Schmidhuber, J{\"u}rgen},
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title = {Deep, Big, Simple Neural Nets for Handwritten Digit
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Recognition},
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journal = {Neural Computation},
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year = 2010,
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month = {Dec},
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day = 01,
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volume = 22,
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number = 12,
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pages = {3207-3220},
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abstract = {Good old online backpropagation for plain multilayer
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perceptrons yields a very low 0.35{\%} error rate on the MNIST
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handwritten digits benchmark. All we need to achieve this best
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result so far are many hidden layers, many neurons per layer,
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numerous deformed training images to avoid overfitting, and
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graphics cards to greatly speed up learning.},
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issn = {0899-7667},
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doi = {10.1162/NECO_a_00052},
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url = {https://doi.org/10.1162/NECO_a_00052}
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}
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