Found 5 packages
A complete C# re-write of Berkeley's open source Convolutional Architecture for Fast Feature Encoding (CAFFE) for Windows C# Developers with full On-line Help, now with Temporal Fusion Transformers, GPT, Seq2Seq/Attention, Single-Shot MultiBox, TripletNet, SiameseNet, NoisyNet, Deep Q-Network and Policy Gradient Reinforcement Learning, cuDNN LSTM Recurrent Learning, and Neural Style Transfer support!
Bright Wire is an open source machine learning library. Includes neural networks (feed forward, convolutional and recurrent), naive bayes, linear regression, decision trees, logistic regression, k-means clustering and dimensionality reduction.
Giving Windows C# developers easy access to the ONNX AI Model Format for easy model conversions.
Bright Wire is an open source machine learning library for .net core. Includes neural networks (feed forward, convolutional and recurrent), naive bayes, linear regression, decision trees, logistic regression, k-means clustering and dimensionality reduction.
DotTorch Layers is a high-performance, modular neural network layers library for .NET 8 and .NET 9. It includes core layers such as Linear, ReLU, Sequential, Dropout, Embedding, Sigmoid, SoftMax, Tanh, LeakyReLU, GELU, ELU, and Flatten. Advanced recurrent layers like RNN, LSTM, and GRU are also implemented, along with powerful Transformer layers. The package features normalization layers: LayerNorm (currently not optimized) and BatchNorm (optimized, with LayerNorm mode support). All layers seamlessly integrate with the DotTorch.Core autograd system, enabling automatic differentiation and backpropagation. Designed for ease of use, extensibility, and efficient execution on CPU and GPU devices. This library supports modern .NET frameworks and follows best practices for maintainability and performance in machine learning model construction.