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...In addition, it consists of easy-to-use mini-batch loaders for operating on many small and single giant graphs, multi GPU-support, DataPipe support, distributed graph learning via Quiver, a large number of common benchmark datasets (based on simple interfaces to create your own), the GraphGym experiment manager, and helpful transforms, both for learning on arbitrary graphs as well as on 3D meshes or point clouds. All it takes is 10-20 lines of code to get started with training a GNN model (see the next section for a quick tour).

...Haiku can be expected to compose with other libraries and work well with the rest of JAX. Similar to Sonnet modules, Haiku modules are Python objects that hold references to their own parameters, other modules, and methods that apply functions on user inputs.

...Sonnet’s programming model revolves around a single concept: modules. These modules can hold references to parameters, other modules and methods that apply some function on the user input. There are a number of predefined modules that already ship with Sonnet, making it quite powerful and yet simple at the same time. Users are also encouraged to build their own modules. Sonnet is designed to be extremely unopinionated about your use of modules. It is simple to understand, and offers clear and focused code.

...The tool manages automated machine learning (AutoML) experiments, dispatches and runs experiments' trial jobs generated by tuning algorithms to search the best neural architecture and/or hyper-parameters in different training environments like Local Machine, Remote Servers, OpenPAI, Kubeflow, FrameworkController on K8S (AKS etc.) DLWorkspace (aka. DLTS) AML (Azure Machine Learning) and other cloud options. NNI provides CommandLine Tool as well as an user friendly WebUI to manage training experiements.

nn_vis is a minimalist visualization tool for neural networks written in Python using OpenGL and Pygame. It provides an interactive, graphical representation of how data flows through neural network layers, offering a unique educational experience for those new to deep learning or looking to explain it visually. By animating input, weights, activations, and outputs, the tool demystifies neural network operations and helps users intuitively grasp complex concepts. Its lightweight codebase is...

...Recent work by Microsoft and Google has shown that data parallel training can be made significantly more efficient by sharding the model parameters and optimizer state across data parallel workers. These ideas are encapsulated in the new FullyShardedDataParallel (FSDP) wrapper provided by fairscale. Fairseq can be extended through user-supplied plug-ins. Models define the neural network architecture and encapsulate all of the learnable parameters. Criterions compute the loss function given the model outputs and targets. Tasks store dictionaries and provide helpers for loading/iterating over Datasets, initializing the Model/Criterion and calculating the loss.

What is QPF 2.6 ? QPF 2.6 (or Quantum Programming framework 2.6) is a free simple and easy to use framework dedicated to supporting programmers who are developing software for the D-wave one series of quantum computers.

ffnet is a fast and easy-to-use feed-forward neural network training solution for python. Many nice features are implemented: arbitrary network connectivity, automatic data normalization, very efficient training tools, network export to fortran code. Now ffnet has also a GUI called ffnetui.

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.

...Bindings to more than 15 programming languages are available. An easy to read introduction article and a reference manual accompanies the library with examples and recommendations on how to use the library. Several graphical user interfaces are also available for the library.

Yann is Yet Another Neural Network. Yann is a library to create fast neural networks. It is also a GUI to easily create, edit, train, execute and investigate networks. Multiple topologies, runtime properties and ensemble learning are supported.

A neural net module written in python. The aim of the project is to provide a large set of neural network types accessed by an API that is easy to use and powerful.