An implementation of multilayer perceptron(MLP) on function approximation.
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Updated
Mar 30, 2019 - Python
An implementation of multilayer perceptron(MLP) on function approximation.
A short and sweet library handling uncertainty in calculations. Can use both standard, probabilistic uncertainties and maximal uncertainties for arbitrary functions over arbitrary variables.
Dash App for visualizing function approximations by polynomials.
This project is a simple implementation of a neural network with gradient descent optimization from scratch. The goal of this project is to demonstrate how a neural network works and how the gradient descent algorithm can be used to optimize its parameters.
This repository contains numerical methods for finding solutions of a nonlinear equation as well as to approximate functions from a dataset of (x, y) points.
• Artificial Intelligence • In this project we aim to train an artificial neural network to approximate a function of a discrete dynamical system.
This project involves approximating a function to solve an optimization problem. Functions can often be costly to write in code. Approximating a function can sometimes save time and money. Especially when the code is iterated many times.
Distributed and Asynchronous Algorithm for Smooth High-dimensional Function Approximation using Orthotope B-splines
MLP network for approximating functions: implementation and experiments
Repository containing python notebooks used to teach the lab classes of the curricular unit "Numerical Methods (M2039)" at FCUP, Portugal, in study year 2023/2024
Function approximation using Multilayer Perceptron (MLP)
Estimation of a non-linear function using neural networks
This is a repository for Coursera Reinforcement Learning Course Notebook ,, these consist of my solutions. Feel Free to take a look , if you are stuck in Course and suggest corrections, if you find any mistake. Also Useful if you are looking for an implementation of RL-Algorithms. ** NOTE THESE NOTEBOOKS DON'T WORK AS THEY DO NOT CONTAIN UTILITY…
Approximating nonlinear functions with low-rank spiking networks
This project was made to showcase a sample example of muli-threading in the C programming language.
This project is a simple animation of Fourier series, approximating a function using a sum of sine and cosine functions.
Seminar project at FER led by Assistant Professor Marko Čupić
Simple linear regressor that tries to approximate a simple function deployed in Tensorflow 2.0 without Keras
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