From 2a1e04cfb4aa264b90521a3752c82dbe01b6cbf5 Mon Sep 17 00:00:00 2001 From: coolneng Date: Fri, 2 Jul 2021 02:47:54 +0200 Subject: [PATCH] Conclude introduction with DL and bioinformatics --- Dissertation.org | 36 +++++++++++++++++++++++--- Dissertation.pdf | Bin 850557 -> 1035657 bytes assets/babathesis.latex | 6 ++++- assets/bibliography.bib | 49 ++++++++++++++++++++++++++++++++++++ assets/figures/ai-ml-dl.png | Bin 0 -> 101946 bytes 5 files changed, 87 insertions(+), 4 deletions(-) create mode 100644 assets/figures/ai-ml-dl.png diff --git a/Dissertation.org b/Dissertation.org index 58b27d3..db83dae 100644 --- a/Dissertation.org +++ b/Dissertation.org @@ -103,7 +103,6 @@ El estudio se basa en la conjetura de que cada aspecto del aprendizaje o cualqui A finales de los años 1960, los investigadores se toparon con numerosos obstáculos al tratar de usar sus sistemas para resolver problemas más complejos. Debido a una carencia de resultados decisivos, la financiación en este ámbito académico fue cancelada. cite:book:771224 Los sistemas de conocimiento (década de 1970) aprovecharon conocimiento específico de una rama para escalar los sistemas inteligentes, intercambiando sistemas generales con bajo rendimiento por sistemas específicos al problema con mejor rendimiento. Estos sistemas novedosos se empezaron a comercializar en los años 80, creando la industria de la IA. cite:book:771224 - ** Redes neuronales artificiales Una red neuronal artificial es un modelo de computación bioinspirado, formado por capas de neuronas artificiales. Comenzaremos definiendo el concepto de neurona artificial, con el fin de introducir la noción de red neuronal artificial de forma clara y concisa. @@ -149,7 +148,7 @@ El /gradient descent/ es una forma de minimizar una función objetivo parametriz #+CAPTION: Diagrama del algoritmo de /gradient descent/. Comenzando en el punto inicial $w^0$ hacemos nuestra primera aproximación a $g(w)$ en el punto $(w^0,g(w^0))$ en la función (mostrada como un punto negro hueco) con la aproximación en serie de Taylor de primer orden dibujada en rojo. Moviéndonos en la dirección de descenso del gradiente negativo proporcionada por esta aproximación llegamos a un punto $w^1=w^0-\alpha\frac{d}{dw}g(w^0)$. A continuación, repetimos este proceso en $w^1$, moviéndonos en la dirección del gradiente negativo allí, y así sucesivamente. cite:book:2530718 #+ATTR_HTML: :height 25% :width 80% -#+NAME: fig:central-dogma +#+NAME: fig:gradient-descent [[./assets/figures/gradient-descent.png]] \clearpage @@ -166,8 +165,39 @@ Este proceso, junto con sus variantes, es el algoritmo de optimización local m ** Hacia el Deep Learning -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/. +El /Deep Learning/ es un subconjunto del /machine learning/, en el cual se utiliza como modelo de computación las redes neuronales artificiales (ANN), con múltiples capas ocultas. Este enfoque trata de resolver el problema de una buena capacidad expresiva de las redes neuronales, sin el aumento exponencial del número de neuronas. cite:DBLP:journals/corr/WangRX17 +#+CAPTION: Diferencia entre inteligencia artificial, /machine learning/ y /deep learning/ +#+ATTR_HTML: :height 25% :width 80% +#+NAME: fig:ai-ml-dl +[[./assets/figures/ai-ml-dl.png]] + +La pregunta sobre cómo mantener esta capacidad expresiva de la red y al mismo tiempo reducir el número de unidades de cálculo se ha planteado durante años. Intuitivamente, se sugirió que es natural buscar redes más profundas porque 1) el sistema neuronal humano es una arquitectura profunda y 2) los humanos tienden a representar conceptos en un nivel de abstracción como la composición de conceptos en niveles inferiores. Actualmente, la solución es construir arquitecturas más profundas, \ie redes neuronales con más capas ocultas. cite:DBLP:journals/corr/WangRX17 + +En la actualidad, los avances tanto en potencia de cálculo del /hardware/, especialmente en las tarjetas gráficas (GPU) cite:Cireşan2010, junto con la disponibilidad de grandes /datasets/ cite:book:771224 han permitido que esta disciplina prospere. + +Este área de conocimiento ha mejorado drásticamente el estado del arte en muchas tareas de inteligencia artificial, como la detección de objetos, el reconocimiento del habla o la traducción automática. Su naturaleza de arquitectura profunda otorga al aprendizaje profundo la posibilidad de resolver muchas tareas de IA más complicadas. cite:DBLP:journals/corr/WangRX17 Como resultado, los investigadores están extendiendo el aprendizaje profundo a una variedad de diferentes dominios y tareas modernas: + +- Eliminación del ruido en las señales de voz +- Descubrimiento de patrones de agrupación de expresiones genéticas +- Generación de imágenes con distintos estilos +- Análisis de sentimientos de diferentes fuentes simultáneas +** Bioinformática + +La bioinformática es un campo interdisciplinar en el que intervienen principalmente la biología molecular y la genética, la informática, las matemáticas y la estadística. Los problemas biológicos a gran escala y con gran cantidad de datos se abordan desde un punto de vista informático. Los problemas más comunes son el modelado de procesos biológicos a nivel molecular y la realización de inferencias a partir de los datos recogidos, cite:Can2014 en particular: + +#+CAPTION: Problemas clásicos de la bioinformática +#+NAME: tab:bioinformatics-problems +| Problema | Fuente de datos | +|--------------------------------------+------------------| +| Alineamiento de secuencias múltiples | ADN | +| Búsqueda de patrones de secuencias | ADN | +| Análisis evolutivos | ADN | +| Análisis de la función | Proteínas | +| Predicción de las estructuras | Proteínas | +| Estudio de las vías metabólicas | Redes biológicas | + +Es tentador atribuir los orígenes de la bioinformática a la reciente convergencia de la secuenciación del ADN, los proyectos genómicos a gran escala, Internet y los superordenadores. Sin embargo, algunos científicos que afirman que la bioinformática se encuentra en su infancia reconocen que los ordenadores eran herramientas importantes en la biología molecular una década antes de que la secuenciación del ADN se convirtiera en algo factible (década de los 1960). cite:Hagen2000 * Estado del arte ** Bioinformática diff --git a/Dissertation.pdf b/Dissertation.pdf index 43f860193fa1f294f0881768696c3da540618c9b..8c69bb49cc3b4529fd6855006d9359ee7e5f68be 100644 GIT binary patch delta 213820 zcmYhhby$?o_dmRJhajSKhm>@)NJuFNk^-)DgVKGYAP7haf^@flfOIU1h=549fFRu+ z%kIv@`}_I*@%%B@bziS@=FFUPPn;OciFkA-JnRe3P0BR(adK{aQBg6mG(}3BJJ8$r znp$05Q3Q2(J3?Royk^=tJ<0L>i3RK_RZWVq#=NHF5ogV;pYGn`?;nHHK70?J*Ew@M zdQDtX1083ZJX>52tipOA;2n>Up+878Z;SZ`w(0c~m;dtSw0h;?3Uz9>yiZyCO%@>3 z{_7LdqO`@W(Aly>;v*Xos0g0}AS-wI{4i=^fUiSmz zW1q;q4LrCX@*?ncMBQtyMj4Li-nU%x&2s#CYmAzA-dFlO^c}@2ra!+~Ii-^C4so_j zC{W(86_VO`r%wRiczYu@rw#GqHg9QT!kRHt*UW9EhhJNmuwAol$AfBKJ4YGK7loCf 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