{"id":60044,"date":"2021-01-14T11:13:27","date_gmt":"2021-01-14T10:13:27","guid":{"rendered":"https:\/\/openexpoeurope.com\/?p=60044"},"modified":"2021-01-14T11:14:47","modified_gmt":"2021-01-14T10:14:47","slug":"deep-learning-julia-o-python","status":"publish","type":"post","link":"https:\/\/openexpoeurope.com\/es\/deep-learning-julia-o-python\/","title":{"rendered":"Deep Learning: \u00bfJulia o Python?"},"content":{"rendered":"

El origen de lenguajes innovadores de programaci\u00f3n, ha hecho que se potencie de alguna manera la arquitectura de c\u00f3digo abierto. Cada d\u00eda somos m\u00e1s conscientes de las caracter\u00edsticas y particularidades del lenguaje de programaci\u00f3n. Por eso siguen surgiendo nuevos lenguajes, para facilitar el trabajo a los desarrolladores. <\/span><\/span><\/span>As\u00ed es que, en el mundo de la inteligencia artificial, es creciente la curiosidad por el lenguaje Python, pero \u00bfos suena de algo Julia?<\/strong><\/span><\/span><\/span><\/p>\n

A continuaci\u00f3n, haremos una breve comparaci\u00f3n entre ambos lenguajes para conocer cu\u00e1l de ellos puede ser m\u00e1s productivo y cu\u00e1les son las diferencias.<\/span><\/span><\/span><\/p>\n

\u00bfQu\u00e9 diferencia a Julia?<\/strong><\/h2>\n

En primer lugar, es un lenguaje de programaci\u00f3n que pretende hacer un \u00fanico lenguaje con toda la eficiencia de los que conocemos. Se puede aplicar tanto en <\/strong><\/span><\/span><\/span>Deep Learning<\/i><\/span><\/span><\/span> como en <\/span><\/span><\/span>Big Data<\/i><\/span><\/span><\/span><\/strong>, adem\u00e1s de \u00e1lgebra lineal a gran escala y tambi\u00e9n computaci\u00f3n distribuida y paralela. Por lo que podemos decir, que es un lenguaje que se lleva bien con la computaci\u00f3n matem\u00e1tica y cient\u00edfica.<\/span><\/span><\/span><\/p>\n

Una de sus principales caracter\u00edsticas es la velocidad, es un programa que pretende facilitar y agilizar el tiempo a los cient\u00edficos y analistas de datos. Sin embargo, esto ocurre en tareas complejas en la nube, tiene una ejecuci\u00f3n m\u00e1s r\u00e1pida que la de Python, es por eso que denota inter\u00e9s en el \u00e1mbito del Big Data.<\/span><\/span><\/span><\/p>\n

Si hablamos de bibliotecas, Flux y Knet hacen que Julia sea m\u00e1s potente en Deep Learning y Machine Learning. Sin embargo, la mayor\u00eda de los proyectos se siguen realizando con Pytorch o TensorFlow. Si bien es cierto que Julia te permite utilizar bibliotecas tanto de Python como de C, quiz\u00e1 necesita desarrollar sus propias librer\u00edas para mejorar.<\/span><\/span><\/span><\/p>\n

\u00bfEn qu\u00e9 sigue por delante Python?<\/strong><\/h2>\n

Python es un lenguaje de programaci\u00f3n m\u00e1s maduro. Se cre\u00f3 principalmente para el desarrollo web y ciencia de datos entre otros. Destaca por su visualizaci\u00f3n, adem\u00e1s permite una mayor manipulaci\u00f3n de cadenas y es ideal para scripts o aplicaciones r\u00e1pidas. Se concilia bien con otras plataformas debido a su madurez, y por eso es m\u00e1s conocido.<\/span><\/span><\/span><\/p>\n

A pesar de todo, Python sigue optimizando, por ejemplo, el procesamiento en paralelo, de hecho, se est\u00e1 volviendo m\u00e1s f\u00e1cil de acelerar.\u00a0<\/span><\/span><\/span><\/p>\n

Un ejemplo es el proyecto mypyc, que se est\u00e1 encargando de producir mejoras de rendimiento. Este proyecto trata de traducir Python con anotaciones en C, todo ello, con operaciones matem\u00e1ticas cada vez m\u00e1s complejas.<\/span><\/span><\/span><\/p>\n

\u00bfQu\u00e9 lenguaje utilizamos si queremos mejorar la productividad en Deep Learning?<\/span><\/span><\/span><\/strong><\/h3>\n

Como hemos hablado, Julia tiene una sintaxis asequible con las matem\u00e1ticas. Esto se debe a que es utilizado por ambientes relacionados con la inform\u00e1tica como Matlab, Octave o R. Es por ello que la sintaxis de Julia, aparentemente, tiene relaci\u00f3n con las matem\u00e1ticas puras, lo que favorece el aprendizaje de no programadores. Tambi\u00e9n debemos tener en cuenta, que Julia no siempre es m\u00e1s r\u00e1pida que Python. Por lo tanto, debemos estudiar bien las t\u00e9cnicas de optimizaci\u00f3n de Python antes de decidir cambiarnos.\u00a0\u00a0<\/span><\/span><\/span><\/p>\n

Despu\u00e9s de conocer las mejoras, est\u00e1 claro que, para cada proyecto, tendremos que conocer de primera mano las optimizaciones de cada lenguaje para mejorar el rendimiento de la inteligencia artificial. Esa ser\u00e1 la mejor manera de optimizar nuestra eficiencia en posibles nuevos proyectos.<\/span><\/span><\/span><\/p>\n

M\u00e1s art\u00edculos de Cristina P\u00e9rez Lozano<\/strong><\/a><\/h3>\n","protected":false},"excerpt":{"rendered":"

El origen de lenguajes innovadores de programaci\u00f3n, ha hecho que se potencie de alguna manera la arquitectura de c\u00f3digo abierto. Cada d\u00eda somos m\u00e1s conscientes de las caracter\u00edsticas y particularidades del lenguaje de programaci\u00f3n. Por eso siguen surgiendo nuevos lenguajes, para facilitar el trabajo a los desarrolladores. As\u00ed es que, en el mundo de la… <\/p>\n

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