Search Results for "backpropagation"

...The project organizes a progressive path through exercises, notebooks, code examples, and practical mini-projects that echo Karpathy’s approach to “learning by doing,” where students build core concepts from first principles rather than consuming superficial abstractions. It covers topics like implementing backpropagation from scratch, understanding convolutional and recurrent networks, building simple training loops, and exploring real datasets with hands-on code. This collection makes abstract theoretical ideas concrete by walking learners through real code and tangible outcomes, helping demystify parts of machine learning that often feel opaque in purely textbook settings.

TLlib is an open-source and well-documented library for Transfer Learning. It is based on pure PyTorch with high performance and friendly API. Our code is pythonic, and the design is consistent with torchvision. You can easily develop new algorithms or readily apply existing algorithms. We appreciate all contributions. If you are planning to contribute back bug-fixes, please do so without any further discussion. If you plan to contribute new features, utility functions or extensions, please...

Neuroph is lightweight Java Neural Network Framework which can be used to develop common neural network architectures. Small number of basic classes which correspond to basic NN concepts, and GUI editor makes it easy to learn and use.

CRF-RNN is a deep neural architecture that integrates fully connected Conditional Random Fields (CRFs) with Convolutional Neural Networks (CNNs) by reformulating mean-field CRF inference as a Recurrent Neural Network. This fusion enables end-to-end training via backpropagation for semantic image segmentation tasks, eliminating the need for separate, offline post-processing steps. Our work allows computers to recognize objects in images, what is distinctive about our work is that we also recover the 2D outline of objects. Currently we have trained this model to recognize 20 classes. This software allows you to test our algorithm on your own images – have a try and see if you can fool it, if you get some good examples you can send them to us. ...

This project includes the implementation of a neural network MLP, RBF, SOM and Hopfield networks in several popular programming languages. The project also includes examples of the use of neural networks as function approximation and time series prediction. Includes a special program makes it easy to test neural network based on training data and the optimization of the network.

Code of this library is partialy based on myCNN MATLAB class written by Nikolay Chemurin.

Cognity is an object-oriented neural network library for Java. It's goal is to provide easy-to-use, high level architecture for neural network computations along with reasonable performance.

A fast artificial intelligence library which currently supports: kNN (k-Nearest Neighbor algorithm) MLP (Multilayer-Perceptron)

3-layer neural network for regression and classification with sigmoid activation function and command line interface similar to LibSVM. Quick Start: "java -jar nen.jar"

Syntactic is a simple C++ library for constructing general Neural networks. For now the library supports creation of multi layered networks for the Feedforward Backpropagation algorithm as well as Time Series Networks. More will be implemented soon.

This project uses massively parallel Graphics Processing Units(GPU) for neural network(Backpropagation) purposes.

Lightweight backpropagation neural network in C++. The project provides a class implementing a feedforward neural network, and a class for easily train it. It is highly customizable to manage your problem and comes with a simple graphical interface.

Lightweight backpropagation neural network in C. Intended for programs that need a simple neural network and do not want needlessly complex neural network libraries. Includes example application that trains a network to recognize handwritten digits.