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.
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Updated
Dec 19, 2020 - Python
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.
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.
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
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.
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
Approximating nonlinear functions with low-rank spiking networks
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
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…
Assignments and Reading Material for RL Course
This is a reposatory for implementation of different types of optimizers (SGD, RMSprop, Adam etc.) with three different use cases Function Approximation, Multi-class Single-label Classification and Multi-class Multi-label Classification)
Reinforcement Learning algorithms
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