Repositorio de los Laboratorios de Análisis Numérico / Análisis Numérico I de FAMAF, UNC.

Overview

Repositorio de los Laboratorios de Análisis Numérico / Análisis Numérico I de FAMAF, UNC.

Para los Laboratorios de la materia, vamos a utilizar el lenguaje de programación Python 3.X (donde X puede ser cualquier número mayor o igual a 5).

  • Tutorial para crear cuentas de UNC alumnos en este enlace.
  • Para instalar Python en sus computadoras, pueden seguir la página https://tutorial.djangogirls.org/es/python_installation/, tiene un buen tutorial para comenzar. En caso de querer una ayuda un poco más visual, recomendamos seguir este video.
  • Para poder crear el código que van a ejecutar, les recomendamos utilizar Visual Studio Code: https://code.visualstudio.com/. Este editor tiene la capacidad de agregar extensiones que facilitan un poco el trabajo con Python, pueden buscar la extensión llamada Python que aparece en la sección de Extensiones una vez que tengan instalado VSCode.
  • Para aquellas personas que solamente puedan usar el celular para realizar los laboratorios, es posible descargar la aplicación Google Colab Android View (Enlace a Google Play), que les permite utilizar y ejecutar código Python en celdas.

Playlists de Clases de Ediciones Anteriores

Owner
Luis Biedma
Mathematician and Data Scientist.
Luis Biedma
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FedMM: Saddle Point Optimization for Federated Adversarial Domain Adaptation

This repository contains the code accompanying the paper " FedMM: Saddle Point Optimization for Federated Adversarial Domain Adaptation" Paper link: R

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simple artificial intelligence utilities

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Chetan Hirapara 3 Oct 07, 2022
[NeurIPS 2021] Deceive D: Adaptive Pseudo Augmentation for GAN Training with Limited Data

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Official PyTorch Implementation of paper EAN: Event Adaptive Network for Efficient Action Recognition

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SciFive: a text-text transformer model for biomedical literature

SciFive SciFive provided a Text-Text framework for biomedical language and natural language in NLP. Under the T5's framework and desrbibed in the pape

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Code for our paper "Interactive Analysis of CNN Robustness"

Perturber Code for our paper "Interactive Analysis of CNN Robustness" Datasets Feature visualizations: Google Drive Fine-tuning checkpoints as saved m

Stefan Sietzen 0 Aug 17, 2021
SubOmiEmbed: Self-supervised Representation Learning of Multi-omics Data for Cancer Type Classification

SubOmiEmbed: Self-supervised Representation Learning of Multi-omics Data for Cancer Type Classification

Sayed Hashim 3 Nov 15, 2022