Search Results for "artificial neural network code"

CourseraMachineLearning is a personal collection of resources, notes, and programming exercises from Andrew Ng’s popular Machine Learning course on Coursera. It consolidates lecture references, programming tutorials, test cases, and supporting materials into one repository for easier review and practice. The project highlights fundamental machine learning concepts such as hypothesis functions, cost functions, gradient descent, bias-variance tradeoffs, and regression models. It also organizes...

CFNet is the official implementation of End-to-end representation learning for Correlation Filter based tracking (CVPR 2017) by Luca Bertinetto, Jack Valmadre, João F. Henriques, Andrea Vedaldi, and Philip H. S. Torr. The framework combines correlation filters with deep convolutional neural networks to create an efficient and accurate visual object tracker. Unlike traditional correlation filter trackers that rely on hand-crafted features, CFNet learns feature representations directly from...

--> For a more detailed description check the README text under the 'Files' menu option :) The project consists of a few very simple .m files for a Basic Neural Networks study under Octave (or Matlab). The idea is to provide a context for beginners that will allow to develop neural networks, while at the same time get to see and feel the behavior of a basic neural networks' functioning. The code is completely open to be modified and may suit several scenarios. The code...

Locally Weighted Projection Regression (LWPR) is a fully incremental, online algorithm for non-linear function approximation in high dimensional spaces, capable of handling redundant and irrelevant input dimensions. At its core, it uses locally linear models, spanned by a small number of univariate regressions in selected directions in input space. A locally weighted variant of Partial Least Squares (PLS) is employed for doing the dimensionality reduction. Please cite: [1] Sethu...

The Stanford Machine Learning Course Exercises repository contains programming assignments from the well-known Stanford Machine Learning online course. It includes implementations of a variety of fundamental algorithms using Python and MATLAB/Octave. The repository covers a broad set of topics such as linear regression, logistic regression, neural networks, clustering, support vector machines, and recommender systems. Each folder corresponds to a specific algorithm or concept, making it easy...

An adaptive neural network and evolutionary algorithms approach to the machine learning tasks, based on the modular graph grammars. Tested on the "two spirals problem" and other tasks. Implemented in Matlab and C++.

The project goal is to develop several IP cores that would implement artificial neural networks using FPGA resources. These cores will be designed in such a way to allow easy integration in the Xilinx EDK framework.