function-approximation

Instant neural graphics primitives: lightning fast NeRF and more

Library for multivariate function approximation with splines (B-spline, P-spline, and more) with interfaces to C++, C, Python and MATLAB

Fast, memory-efficient 3D spline interpolation and global kriging, via RBF (radial basis function) interpolation.

CSE 571 Artificial Intelligence

Reinforcement learning algorithms

Julia Wrapper to the Tasmanian library

Adaptively sampled distance fields in Julia

Basis Function Expansions for Julia

A collection of B-spline tools in Julia

Julia library for function approximation with compact basis functions

The tools for proper interactions between ApproxFun.jl and DifferentialEquations.jl for pseudospectiral partial differential equation discretizations in scientific machine learning (SciML)

Multivariate Normal Hermite-Birkhoff Interpolating Splines in Julia

An adaptive fast function approximator based on tree search

Easy21 assignment from David Silver's RL Course at UCL

Universal Function Approximation by Neural Nets

Reinforcement Learning algorithms

Course work of Reinforcement-Learning-CS6700

Seminar project at FER led by Assistant Professor Marko Čupić

Many different Neural Networks in Python Language. This repository is an independent work, it is related to my 'Redes Neuronales' repo, but here I'll use only Python.

MLP network for approximating functions: implementation and experiments

Assignments and Reading Material for RL Course

Practical experiments on Machine Learning in Python. Processing of sentences and finding relevant ones, approximation of function with polynomials, function optimization

Suite of 1D, 2D, 3D demo apps of varying complexity with built-in support for sample mesh and exact Jacobians

Python framework to approximate mathemtical functions

An implementation of multilayer perceptron(MLP) on function approximation.

Approximation of mixing different gaussian distribution with Self-organizing Map(SoM) and Radial Basis Function(RBF)

My Machine Learning course projects

The focus of function approximation problems has been on identifying some suitable function without attempting to gain insight into the mechanism of the system. The performance of the model boils down to interpolation. But, in a more realistic setting, we expect test data from outside the distribution of the training set. To better extrapolate to unseen domains, it is essential to learn the correct underlying equations of the system. The Equation Learner (EQL) Network attempts to achieve this task.

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 FILES WHICH ARE AVAILABLE ONLY ON COURSERA.