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SantiagoSeguraR's Projects

creaci-n-de-distintos-modelos-de-redes-neuronales. icon creaci-n-de-distintos-modelos-de-redes-neuronales.

Has equipo con uno de tus compañeros de clase. Seleccionen en Personas uno de los grupos creados para realizar la actividad en Canvas y agreguen sus nombres. Visiten y prueben el playground de Tensorflow: https://playground.tensorflow.org/ (Enlaces a un sitio externo.) Realicen y modifiquen los siguientes tutoriales para creación de distintos modelos de redes neuronales supervisadas: nn_backprop.ipynb Descargar nn_backprop.ipynb classification.ipynb Descargar classification.ipynb regression.ipynb Descargar regression.ipynb Realicen y modifiquen los siguientes tutoriales para creación de distintos modelos de redes neuronales de aprendizaje competitivo: som.ipynb Descargar som.ipynb LVQ.ipynb Descargar LVQ.ipynbutilizando el dataset ionosphere.csv Descargar ionosphere.csv Las modificaciones deben consistir en modificaciones de los datasets, cuando apropiado, los componentes de la red, la arquitectura y/o hiper-parámetros. Escribir un documento de reflexión en equipo donde describan sus experiencias, dificultades, resultados y conclusiones sobre estas actividades. Suban el notebook (.ipynb) y el archivo pdf de su solución a Canvas.

datasciencecoursera icon datasciencecoursera

The Course Project is pretty straightforward. Now that you’ve learned about the basic tools found in the data scientist’s toolbox, we want to make sure that you’ve assembled those tools and are ready to begin using them. This easy project is your chance to demonstrate that you’ve done the basic software setup (R, RStudio, and Github) that will get you through the rest of the Data Science Specialization.

simulated-annealing icon simulated-annealing

This repository contains a python code of simulated annealing to solve a travelling salesman problem.

travelling_salesman_optimization icon travelling_salesman_optimization

Python implementation for TSP using Genetic Algorithms, Simulated Annealing, PSO (Particle Swarm Optimization), Dynamic Programming, Brute Force, Greedy and Divide and Conquer

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