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

Last update: Dec 12, 2022

Related tags

Deep Learning anfamaf2022

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

GitHub Repository

Torch-based tool for quantizing high-dimensional vectors using additive codebooks

Trainable multi-codebook quantization This repository implements a utility for use with PyTorch, and ideally GPUs, for training an efficient quantizer

41 Jan 07, 2023

Demystifying How Self-Supervised Features Improve Training from Noisy Labels

Demystifying How Self-Supervised Features Improve Training from Noisy Labels This code is a PyTorch implementation of the paper "[Demystifying How Sel

[email protected]"> 4 Oct 14, 2022

Learning to See by Looking at Noise

Learning to See by Looking at Noise This is the official implementation of Learning to See by Looking at Noise. In this work, we investigate a suite o

82 Dec 24, 2022

Pytorch implementation of COIN, a framework for compression with implicit neural representations 🌸

COIN 🌟 This repo contains a Pytorch implementation of COIN: COmpression with Implicit Neural representations, including code to reproduce all experim

104 Dec 14, 2022

Solving reinforcement learning tasks which require language and vision

Multimodal Reinforcement Learning JAX implementations of the following multimodal reinforcement learning approaches. Dual-coding Episodic Memory from

31 Feb 26, 2022

TimeSHAP explains Recurrent Neural Network predictions.

TimeSHAP TimeSHAP is a model-agnostic, recurrent explainer that builds upon KernelSHAP and extends it to the sequential domain. TimeSHAP computes even

90 Dec 18, 2022

Viewmaker Networks: Learning Views for Unsupervised Representation Learning

Viewmaker Networks: Learning Views for Unsupervised Representation Learning Alex Tamkin, Mike Wu, and Noah Goodman Paper link: https://arxiv.org/abs/2

31 Dec 01, 2022

This is the code repository for the paper A hierarchical semantic segmentation framework for computer-vision-based bridge column damage detection

Bridge-damage-segmentation This is the code repository for the paper A hierarchical semantic segmentation framework for computer-vision-based bridge c

5 Dec 07, 2022

Hooks for VCOCO

Verbs in COCO (V-COCO) Dataset This repository hosts the Verbs in COCO (V-COCO) dataset and associated code to evaluate models for the Visual Semantic

131 Nov 24, 2022

Self-Regulated Learning for Egocentric Video Activity Anticipation

Self-Regulated Learning for Egocentric Video Activity Anticipation Introduction This is a Pytorch implementation of the model described in our paper:

13 Sep 23, 2022

a reimplementation of Optical Flow Estimation using a Spatial Pyramid Network in PyTorch

pytorch-spynet This is a personal reimplementation of SPyNet [1] using PyTorch. Should you be making use of this work, please cite the paper according

269 Jan 02, 2023

A set of tests for evaluating large-scale algorithms for Wasserstein-2 transport maps computation.

Continuous Wasserstein-2 Benchmark This is the official Python implementation of the NeurIPS 2021 paper Do Neural Optimal Transport Solvers Work? A Co

22 Dec 12, 2022

A novel method to tune language models. Codes and datasets for paper ``GPT understands, too''.

P-tuning A novel method to tune language models. Codes and datasets for paper ``GPT understands, too''. How to use our code We have released the code

562 Dec 27, 2022

f-BRS: Rethinking Backpropagating Refinement for Interactive Segmentation

f-BRS: Rethinking Backpropagating Refinement for Interactive Segmentation [Paper] [PyTorch] [MXNet] [Video] This repository provides code for training

516 Dec 21, 2022

PyTorch implementation of Hierarchical Multi-label Text Classification: An Attention-based Recurrent Network

hierarchical-multi-label-text-classification-pytorch Hierarchical Multi-label Text Classification: An Attention-based Recurrent Network Approach This

17 Dec 13, 2022

TensorFlow, PyTorch and Numpy layers for generating Orthogonal Polynomials

OrthNet TensorFlow, PyTorch and Numpy layers for generating multi-dimensional Orthogonal Polynomials 1. Installation 2. Usage 3. Polynomials 4. Base C

29 May 25, 2022

Implicit Deep Adaptive Design (iDAD)

Implicit Deep Adaptive Design (iDAD) This code supports the NeurIPS paper 'Implicit Deep Adaptive Design: Policy-Based Experimental Design without Lik

12 Aug 14, 2022

A python code to convert Keras pre-trained weights to Pytorch version

Weights_Keras_2_Pytorch 最近想在Pytorch项目里使用一下谷歌的NIMA，但是发现没有预训练好的pytorch权重，于是整理了一下将Keras预训练权重转为Pytorch的代码，目前是支持Keras的Conv2D, Dense, DepthwiseConv2D, Batch

2 Dec 16, 2021

Multimodal Temporal Context Network (MTCN)

Multimodal Temporal Context Network (MTCN) This repository implements the model proposed in the paper: Evangelos Kazakos, Jaesung Huh, Arsha Nagrani,

13 Nov 24, 2022

Must-read Papers on Physics-Informed Neural Networks.

PINNpapers Contributed by IDRL lab. Introduction Physics-Informed Neural Network (PINN) has achieved great success in scientific computing since 2017.

330 Jan 07, 2023