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franklinic edited this page Jan 19, 2026
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Welcome to the official AiDotNet documentation wiki! AiDotNet is the most comprehensive AI/ML framework for .NET, featuring 100+ neural network architectures, 106+ classical ML algorithms, and much more.
| Resource | Description |
|---|---|
| Getting Started | Install AiDotNet and build your first model |
| Installation Guide | Detailed installation instructions |
| Interactive Playground | Try AiDotNet in your browser |
| API Reference | Auto-generated API documentation |
| GitHub Repository | Source code and issues |
- Feedforward: Dense, MLP, Residual connections
- Convolutional: CNN, ResNet, VGG, EfficientNet, MobileNet
- Recurrent: RNN, LSTM, GRU, Bidirectional
- Transformers: Attention, Multi-head attention, Vision Transformer
- Generative: GAN, VAE, Diffusion models
- Graph: GNN, GAT, GraphSAGE
- Specialized: Capsule networks, NeRF, Neural ODE
Learn more about Neural Networks
- Classification (28): SVM, Random Forest, Gradient Boosting, Naive Bayes, KNN, Decision Trees
- Regression (41): Linear, Ridge, Lasso, ElasticNet, SVR, Gaussian Process
- Clustering (20+): K-Means, DBSCAN, HDBSCAN, Spectral, Hierarchical
- Dimensionality Reduction: PCA, t-SNE, UMAP, LDA
- Object Detection: YOLO v8-11, DETR, Faster R-CNN
- Segmentation: Mask R-CNN, U-Net, SAM
- Classification: ResNet, EfficientNet, Vision Transformer
- OCR: Text detection and recognition
Learn more about Computer Vision
- Speech Recognition: Whisper, Wav2Vec2
- Text-to-Speech: Multiple TTS engines
- Audio Classification: Sound event detection
- Music Generation: AudioLDM, MusicGen
Learn more about Audio Processing
- Text Classification: Sentiment, topic classification
- Named Entity Recognition: NER models
- Embeddings: Sentence transformers, word embeddings
- Tokenization: BPE, WordPiece, SentencePiece
- Vector Stores: In-memory, persistent stores
- Retrievers: Dense, sparse, hybrid retrieval
- Rerankers: Cross-encoder reranking
- Chunking: Semantic, recursive chunking
- Standard LoRA: Low-rank adaptation
- QLoRA: Quantized LoRA
- DoRA: Weight-decomposed LoRA
- AdaLoRA: Adaptive LoRA
- Value-based: DQN, Double DQN, Dueling DQN
- Policy Gradient: PPO, A2C, A3C, TRPO
- Actor-Critic: SAC, TD3, DDPG
- Model-based: World models, MuZero
- Multi-Agent: MADDPG, QMIX
Learn more about Reinforcement Learning
- Data Parallel: DDP, DistributedDataParallel
- Model Parallel: Pipeline, Tensor parallelism
- ZeRO: ZeRO-1, ZeRO-2, ZeRO-3
- Cloud: vast.ai, RunPod integration
Learn more about Distributed Training
| Task | Tutorial |
|---|---|
| Binary Classification | Sentiment Analysis |
| Multi-class Classification | Iris Classification |
| Regression | House Price Prediction |
| Image Classification | CIFAR-10 with CNN |
| Object Detection | YOLO Object Detection |
| Text Generation | RAG Chatbot |
| Fine-tuning | LoRA Fine-tuning |
| Reinforcement Learning | CartPole with DQN |
| Feature | AiDotNet | ML.NET | TorchSharp | TensorFlow.NET |
|---|---|---|---|---|
| Neural Network Architectures | 100+ | ~10 | Via PyTorch | Via TensorFlow |
| Classical ML Algorithms | 106+ | ~30 | Limited | Limited |
| Native .NET | Yes | Yes | Wrapper | Wrapper |
| HuggingFace Integration | Yes | No | Partial | No |
| LoRA/PEFT Support | 37+ variants | No | Manual | No |
| Distributed Training | Full stack | Limited | Via PyTorch | Via TensorFlow |
- Issues: GitHub Issues
- Discussions: GitHub Discussions
- FAQ: Frequently Asked Questions
- Troubleshooting: Common Issues
Want to contribute? See our Contributing Guide.
AiDotNet is licensed under the MIT License. See LICENSE for details.
Getting Started
Core Concepts
Reference
Community