AiDotNet

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Why AiDotNet?

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Quick Start by Task

What do you want to build?

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Installation

dotnet add package AiDotNet

Requirements: .NET 10.0 / .NET 8.0+ (or .NET Framework 4.7.1+)

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Hello World Example

using AiDotNet;
using AiDotNet.Enums;
using AiDotNet.LinearAlgebra;
using AiDotNet.NeuralNetworks;

// features: [N, 4] tensor of Iris measurements
// labels:   [N, 3] one-hot tensor of species
var architecture = new NeuralNetworkArchitecture<double>(
    inputFeatures: 4, numClasses: 3, complexity: NetworkComplexity.Simple);

var result = await new AiModelBuilder<double, Tensor<double>, Tensor<double>>()
    .ConfigureModel(new NeuralNetwork<double>(architecture))
    .BuildAsync(features, labels);

var predictions = result.Predict(testFeatures);

Full runnable version (inline Iris dataset, train/test split, accuracy report): samples/getting-started/HelloWorld/Program.cs

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Complete Feature Reference

Neural Networks (160+ Architectures)

Click to expand all neural network types

Core Architectures

• NeuralNetwork - Feedforward networks
• ConvolutionalNeuralNetwork - CNNs for images
• RecurrentNeuralNetwork - RNNs for sequences
• LSTMNeuralNetwork - Long Short-Term Memory
• GRUNeuralNetwork - Gated Recurrent Units
• TransformerArchitecture - Attention-based models
• ResNetNetwork - Residual networks
• DenseNetNetwork - Densely connected networks
• EfficientNetNetwork - Efficient scaling
• MobileNetV2Network, MobileNetV3Network - Mobile-optimized

Generative Models

• GenerativeAdversarialNetwork (GAN)
• DCGAN, ConditionalGAN, CycleGAN
• ProgressiveGAN, BigGAN, StyleGAN
• ACGAN, InfoGAN, Pix2Pix
• Autoencoder, VariationalAutoencoder

Graph Neural Networks

• GraphNeuralNetwork (GNN)
• GraphAttentionNetwork (GAT)
• GraphSAGENetwork
• GraphIsomorphismNetwork (GIN)
• GraphGenerationModel

Specialized Architectures

• CapsuleNetwork - Capsule networks
• SpikingNeuralNetwork - Neuromorphic computing
• QuantumNeuralNetwork - Quantum ML
• HyperbolicNeuralNetwork - Hyperbolic geometry
• MixtureOfExpertsNeuralNetwork - MoE
• NeuralTuringMachine - NTM
• DifferentiableNeuralComputer - DNC
• MemoryNetwork - Memory-augmented
• EchoStateNetwork - Reservoir computing
• LiquidStateMachine - Liquid state machines
• HopfieldNetwork - Associative memory
• RestrictedBoltzmannMachine - RBM
• DeepBeliefNetwork - DBN
• DeepBoltzmannMachine - DBM
• RadialBasisFunctionNetwork - RBF
• SelfOrganizingMap - SOM
• ExtremeLearningMachine - ELM
• NEAT - Neuroevolution

Vision-Language Models

• ClipNeuralNetwork - CLIP
• BlipNeuralNetwork, Blip2NeuralNetwork - BLIP
• LLaVANeuralNetwork - LLaVA
• FlamingoNeuralNetwork - Flamingo
• Gpt4VisionNeuralNetwork - GPT-4V

Attention Mechanisms

• AttentionNetwork
• FlashAttention - Memory-efficient attention
• MultiHeadAttention

Example:

var model = new AiModelBuilder<double, Tensor<double>, Tensor<double>>()
    .ConfigureModel(new ConvolutionalNeuralNetwork<double>(
        inputChannels: 3,
        numClasses: 1000,
        architecture: CNNArchitecture.ResNet50))
    .ConfigureOptimizer(new AdamWOptimizer<double>(learningRate: 0.001))
    .ConfigureMixedPrecision()  // FP16 training
    .ConfigureGpuAcceleration()
    .BuildAsync(trainImages, trainLabels);

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Classification (50 Algorithms)

Click to expand all classification algorithms

Ensemble Methods

• RandomForestClassifier
• GradientBoostingClassifier
• AdaBoostClassifier
• ExtraTreesClassifier
• BaggingClassifier
• StackingClassifier
• VotingClassifier

Naive Bayes

• GaussianNaiveBayes
• MultinomialNaiveBayes
• BernoulliNaiveBayes
• ComplementNaiveBayes
• CategoricalNaiveBayes

Linear Models

• LogisticRegression
• RidgeClassifier
• SGDClassifier
• PassiveAggressiveClassifier
• PerceptronClassifier

Support Vector Machines

• LinearSupportVectorClassifier
• SupportVectorClassifier

Discriminant Analysis

• LinearDiscriminantAnalysis
• QuadraticDiscriminantAnalysis

Multi-label/Multi-output

• OneVsRestClassifier
• OneVsOneClassifier
• ClassifierChain
• MultiOutputClassifier

Neighbors

• KNeighborsClassifier

Example:

var result = await new AiModelBuilder<double, double[], double>()
    .ConfigureModel(new RandomForestClassifier<double>(nEstimators: 100))
    .ConfigurePreprocessing(pipeline => pipeline
        .Add(new StandardScaler<double>())
        .Add(new SimpleImputer<double>()))
    .ConfigureCrossValidation(new KFoldCrossValidator<double>(k: 5))
    .BuildAsync(features, labels);

Console.WriteLine($"Accuracy: {result.CrossValidationResult?.MeanAccuracy:P2}");

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Regression (57 Algorithms)

Click to expand all regression algorithms

Linear Models

• MultipleRegression
• PolynomialRegression
• RidgeRegression
• LassoRegression
• ElasticNetRegression
• BayesianRegression
• OrthogonalRegression

Tree-Based

• DecisionTreeRegression
• GradientBoostingRegression
• AdaBoostR2Regression
• ExtremelyRandomizedTreesRegression
• M5ModelTreeRegression
• ConditionalInferenceTreeRegression

Kernel Methods

• GaussianProcessRegression
• KernelRidgeRegression
• SupportVectorRegression

Specialized

• IsotonicRegression
• LocallyWeightedRegression
• PartialLeastSquaresRegression
• MultivariateRegression
• GeneralizedAdditiveModelRegression

Probabilistic

• PoissonRegression
• NegativeBinomialRegression
• QuantileRegression
• RobustRegression

Neural Network Based

• MultilayerPerceptronRegression
• NeuralNetworkRegression

Optimization-Based

• GeneticAlgorithmRegression

Example:

var result = await new AiModelBuilder<double, double[], double>()
    .ConfigureModel(new GradientBoostingRegression<double>(
        nEstimators: 200,
        maxDepth: 5,
        learningRate: 0.1))
    .ConfigureHyperparameterOptimizer(
        new BayesianOptimizer<double>(),
        searchSpace: new HyperparameterSearchSpace()
            .AddContinuous("learning_rate", 0.01, 0.3)
            .AddInteger("max_depth", 3, 10),
        trials: 50)
    .BuildAsync(features, targets);

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Clustering (48 Algorithms)

Click to expand all clustering algorithms

Centroid-Based

• KMeansClustering
• KMedoidsClustering
• MiniBatchKMeans

Density-Based

• DBSCAN
• HDBSCAN
• OPTICS
• MeanShift
• Denclue

Hierarchical

• AgglomerativeClustering
• BIRCH
• CURE

Model-Based

• GaussianMixtureClustering
• BayesianGaussianMixture

Spectral

• SpectralClustering

Self-Organizing

• GMeans
• XMeans

Distance Metrics

• EuclideanDistance
• ManhattanDistance
• CosineDistance
• MahalanobisDistance
• ChebyshevDistance
• MinkowskiDistance

Validation Metrics

• SilhouetteScore
• DaviesBouldinIndex
• CalinskiHarabaszIndex
• DunnIndex
• AdjustedRandIndex

Example:

var result = await new AiModelBuilder<double, double[], int>()
    .ConfigureModel(new HDBSCAN<double>(minClusterSize: 15, minSamples: 5))
    .ConfigureAutoML(new ClusteringAutoML<double>())  // Auto-tune parameters
    .BuildAsync(features);

Console.WriteLine($"Clusters found: {result.Model.Labels.Distinct().Count()}");
Console.WriteLine($"Silhouette Score: {result.ClusteringMetrics?.SilhouetteScore:F3}");

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Computer Vision (115+ Models)

Click to expand all computer vision capabilities

Object Detection

• YOLO Family: YOLOv5, YOLOv8, YOLOv9, YOLOv10, YOLOv11
• Transformer-Based: DETR, Deformable DETR, DINO
• Two-Stage: Faster R-CNN, Cascade R-CNN
• Anchor-Free: FCOS, CenterNet

Instance Segmentation

• Mask R-CNN
• YOLACT
• SOLOv2
• Segment Anything (SAM)

Semantic Segmentation

• DeepLabV3+
• UNet, UNet++
• PSPNet
• HRNet

Object Tracking

• SORT
• DeepSORT
• ByteTrack
• OC-SORT

OCR & Scene Text

• SceneTextReader
• Text detection + recognition
• Multi-language support

Pose Estimation

• OpenPose
• HRNet-Pose
• ViTPose

3D Vision

• PointNet, PointNet++
• MeshCNN
• NeRF (Neural Radiance Fields)

Example:

var builder = new AiModelBuilder<float, Tensor<float>, DetectionResult[]>()
    .ConfigureObjectDetector(new YOLOv8Detector<float>(
        modelSize: YOLOModelSize.Medium,
        confidenceThreshold: 0.5f))
    .ConfigureVisualization(new VisualizationOptions { DrawLabels = true });

var result = await builder.BuildAsync();
var detections = result.Model.Detect(image);

foreach (var det in detections)
    Console.WriteLine($"{det.Label}: {det.Confidence:P1} at {det.BoundingBox}");

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Audio Processing (250+ Models)

Click to expand all audio capabilities

Speech Recognition

• Whisper: whisper-tiny, whisper-base, whisper-small, whisper-medium, whisper-large
• Wav2Vec2: Multiple languages
• HuBERT
• Conformer

Text-to-Speech

• VITS
• FastSpeech2
• Tacotron2
• XTTS (multi-speaker, cross-lingual)

Music Generation

• MusicGen
• AudioGen
• Riffusion

Audio Classification

• Audio event detection
• Music genre classification
• Environmental sound classification

Speaker Analysis

• Speaker identification
• Speaker verification
• Speaker diarization

Audio Enhancement

• Noise reduction
• Echo cancellation
• Speech enhancement

Source Separation

• Vocals/instruments separation
• Multi-track separation

Voice Activity Detection

• WebRTC VAD
• Silero VAD

Emotion Recognition

• Speech emotion classification

Music Analysis

• Beat detection
• Chord recognition
• Key detection
• Tempo estimation

Example:

// Speech-to-Text with Whisper
var whisper = new WhisperModel<float>(WhisperModelSize.Medium, language: "en");
var transcription = await whisper.TranscribeAsync(audioFile);
Console.WriteLine(transcription.Text);

// Text-to-Speech
var tts = new VITSModel<float>(voice: "en-US-female");
var audio = await tts.SynthesizeAsync("Hello, world!");
await audio.SaveAsync("output.wav");

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Video Processing (80+ Models)

Click to expand all video capabilities

Video Generation

• Stable Video Diffusion
• AnimateDiff
• VideoCrafter
• Text2Video

Action Recognition

• SlowFast
• TimeSformer
• Video Swin Transformer

Video Understanding

• CLIP4Clip
• Video captioning

Optical Flow

• RAFT
• FlowNet

Video Object Detection

• Video object tracking
• Multi-object tracking

Example:

var videoGen = new StableVideoDiffusion<float>();
var video = await videoGen.GenerateAsync(
    prompt: "A cat playing piano",
    numFrames: 24,
    fps: 8);
await video.SaveAsync("output.mp4");

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Reinforcement Learning (50+ Agents)

Click to expand all RL agents

Value-Based

• DQNAgent - Deep Q-Network
• DoubleDQNAgent - Double DQN
• DuelingDQNAgent - Dueling architecture
• RainbowDQNAgent - Rainbow (all improvements)
• QLearningAgent, DoubleQLearningAgent
• SARSAAgent, ExpectedSARSAAgent
• NStepQLearningAgent, NStepSARSAAgent

Policy Gradient

• REINFORCEAgent
• A2CAgent - Advantage Actor-Critic
• A3CAgent - Asynchronous A3C
• PPOAgent - Proximal Policy Optimization

Actor-Critic

• DDPGAgent - Deep Deterministic PG
• TD3Agent - Twin Delayed DDPG
• SACAgent - Soft Actor-Critic

Model-Based

• DreamerAgent - World models
• MuZeroAgent - MuZero
• DynaQAgent, DynaQPlusAgent
• PrioritizedSweepingAgent

Multi-Agent

• MADDPGAgent - Multi-Agent DDPG
• QMIXAgent - QMIX

Offline RL

• CQLAgent - Conservative Q-Learning
• IQLAgent - Implicit Q-Learning

Monte Carlo

• MonteCarloExploringStartsAgent
• FirstVisitMonteCarloAgent
• EveryVisitMonteCarloAgent
• OffPolicyMonteCarloAgent

Planning

• MCTSNode - Monte Carlo Tree Search
• PolicyIterationAgent
• ModifiedPolicyIterationAgent

Bandits

• EpsilonGreedyBanditAgent
• UCBBanditAgent
• ThompsonSamplingAgent
• GradientBanditAgent

Sequence Models

• DecisionTransformerAgent

Example:

var env = new CartPoleEnvironment();
var agent = new PPOAgent<double>(
    stateSize: env.ObservationSpace,
    actionSize: env.ActionSpace,
    hiddenSize: 64,
    learningRate: 3e-4);

// Training loop
for (int episode = 0; episode < 1000; episode++)
{
    var state = env.Reset();
    double totalReward = 0;

    while (!env.IsDone)
    {
        var action = agent.SelectAction(state);
        var (nextState, reward, done) = env.Step(action);
        agent.Store(state, action, reward, nextState, done);
        state = nextState;
        totalReward += reward;
    }

    agent.Train();
    Console.WriteLine($"Episode {episode}: Reward = {totalReward}");
}

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Time Series (30+ Models)

Click to expand all time series models

Classical Statistical

• ARModel - Autoregressive
• MAModel - Moving Average
• ARMAModel - ARMA
• ARIMAModel - ARIMA
• SARIMAModel - Seasonal ARIMA
• ARIMAXModel - ARIMAX with exogenous variables
• GARCHModel - Volatility modeling
• VARModel, VARMAModel - Vector models

Exponential Smoothing

• ExponentialSmoothingModel
• HoltWintersModel
• TBATSModel

Deep Learning

• NBEATSModel - N-BEATS
• NHiTSModel - N-HiTS
• DeepARModel - DeepAR
• TemporalFusionTransformer
• InformerModel - Informer
• AutoformerModel - Autoformer
• ChronosFoundationModel - Chronos

Anomaly Detection

• DeepANT
• LSTMVAE
• TimeSeriesIsolationForest

Specialized

• ProphetModel - Facebook Prophet
• StateSpaceModel
• BayesianStructuralTimeSeriesModel
• SpectralAnalysisModel

Example:

var result = await new AiModelBuilder<double, double[], double>()
    .ConfigureModel(new NBEATSModel<double>(
        stackTypes: new[] { StackType.Trend, StackType.Seasonality, StackType.Generic },
        horizonSize: 24))
    .ConfigureTrainingPipeline(new TrainingPipelineConfiguration<double>
    {
        EarlyStopping = new EarlyStoppingConfig { Patience = 10 }
    })
    .BuildAsync(historicalData);

var forecast = result.Model.Forecast(steps: 24);

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Retrieval-Augmented Generation (50+ Components)

Click to expand all RAG components

Vector Stores

• In-memory vector store
• FAISS integration
• Pinecone, Weaviate, Qdrant adapters
• Chroma, Milvus support

Embedding Models

• OpenAI embeddings
• HuggingFace embeddings
• Sentence Transformers
• BGE, ColBERT

Retrievers

• Dense retriever
• Sparse retriever (BM25)
• Hybrid retriever
• Multi-query retriever
• Self-query retriever

Rerankers

• Cross-encoder reranker
• ColBERT reranker
• Cohere reranker

Document Processing

• Text splitters (recursive, semantic)
• Document loaders
• Chunking strategies

Graph RAG

• Knowledge graph construction
• Entity extraction
• Relation extraction
• Graph traversal retrieval

Query Processing

• Query expansion
• Query rewriting
• HyDE (Hypothetical Document Embeddings)

Example:

var result = await new AiModelBuilder<float, string, string>()
    .ConfigureRetrievalAugmentedGeneration(
        retriever: new HybridRetriever<float>(
            denseRetriever: new DenseRetriever<float>(embeddingModel),
            sparseRetriever: new BM25Retriever(),
            alpha: 0.7f),
        reranker: new CrossEncoderReranker<float>(),
        generator: new LLMGenerator<float>(llmClient),
        queryProcessors: new[] { new QueryExpander() })
    .BuildAsync();

var answer = await result.Model.QueryAsync("What is the capital of France?", documents);

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LoRA Fine-tuning (50+ Adapters)

Click to expand all LoRA variants

Standard LoRA

• LoRAAdapter - Original LoRA
• LoRAPlusAdapter - LoRA+

Quantized

• QLoRAAdapter - 4-bit quantization
• QALoRAAdapter - Quantization-aware

Memory Efficient

• DoRAAdapter - Weight-Decomposed
• VeRAAdapter - Very efficient
• NOLAAdapter - Noise-optimized

Rank Adaptation

• AdaLoRAAdapter - Adaptive rank
• DyLoRAAdapter - Dynamic rank
• ReLoRAAdapter - Recursive

Specialized

• LoHaAdapter - Hadamard product
• LoKrAdapter - Kronecker product
• MoRAAdapter - Mixture of ranks
• LongLoRAAdapter - Long context
• GraphConvolutionalLoRAAdapter - For GNNs

Advanced

• PiSSAAdapter - Principal singular values
• FloraAdapter - Floating-point
• DeltaLoRAAdapter - Delta updates
• LoftQAdapter - Quantization-aware init
• ChainLoRAAdapter - Chained adapters

Example:

var result = await new AiModelBuilder<float, string, string>()
    .ConfigureLoRA(new QLoRAConfiguration<float>
    {
        Rank = 16,
        Alpha = 32,
        TargetModules = new[] { "q_proj", "v_proj", "k_proj", "o_proj" },
        QuantizationBits = 4,
        UseDoubleQuantization = true
    })
    .ConfigureFineTuning(new FineTuningConfiguration<float>
    {
        BaseModel = "meta-llama/Llama-2-7b-hf",
        TrainingArguments = new TrainingArguments
        {
            NumEpochs = 3,
            BatchSize = 4,
            GradientAccumulationSteps = 8
        }
    })
    .BuildAsync(trainingData);

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Diffusion Models (75+ Models)

Click to expand all diffusion models

Image Generation

• StableDiffusionModel
• SDXLModel - Stable Diffusion XL
• DallE3Model - DALL-E 3
• PixArtModel - PixArt

Image Editing

• ControlNetModel
• IPAdapterModel
• Inpainting, Outpainting

Audio Generation

• AudioLDMModel, AudioLDM2Model
• MusicGenModel
• RiffusionModel
• DiffWaveModel

Video Generation

• StableVideoDiffusion
• AnimateDiffModel
• VideoCrafterModel

3D Generation

• DreamFusionModel
• MVDreamModel
• ShapEModel
• PointEModel
• Zero123Model

Architectures

• DDPMModel - Denoising Diffusion
• ConsistencyModel - Fast inference
• Various schedulers (DDIM, PNDM, etc.)

Example:

var diffusion = new SDXLModel<float>();
var image = await diffusion.GenerateAsync(
    prompt: "A photorealistic cat astronaut on Mars",
    negativePrompt: "blurry, low quality",
    width: 1024,
    height: 1024,
    numInferenceSteps: 30,
    guidanceScale: 7.5f);

await image.SaveAsync("output.png");

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Distributed Training

Click to expand distributed training options

Data Parallelism

• DDPModel - Distributed Data Parallel
• DDPOptimizer

Model Parallelism

• PipelineParallelModel
• TensorParallelModel

Fully Sharded

• FSDPModel - Fully Sharded Data Parallel
• ZeROOptimizer (Stage 1, 2, 3)
• HybridShardedModel

Communication

• NCCL backend (NVIDIA)
• Gloo backend
• MPI backend

Optimization

• GradientCompressionOptimizer
• AsyncSGDOptimizer
• LocalSGDOptimizer
• ElasticOptimizer - Fault-tolerant

Example:

var result = await new AiModelBuilder<float, Tensor<float>, Tensor<float>>()
    .ConfigureModel(largeModel)
    .ConfigureDistributedTraining(
        strategy: DistributedStrategy.FSDP,
        backend: new NCCLCommunicationBackend(),
        configuration: new FSDPConfiguration
        {
            ShardingStrategy = ShardingStrategy.FullShard,
            MixedPrecision = true,
            ActivationCheckpointing = true
        })
    .ConfigureGpuAcceleration(new GpuAccelerationConfig { DeviceIds = new[] { 0, 1, 2, 3 } })
    .BuildAsync(trainData);

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Meta-Learning (18+ Algorithms)

Click to expand meta-learning methods

Optimization-Based

• MAMLAlgorithm - Model-Agnostic Meta-Learning
• iMAMLAlgorithm - Implicit MAML
• ReptileAlgorithm
• MetaSGDAlgorithm
• ANILAlgorithm - Almost No Inner Loop
• BOILAlgorithm - Body Only Inner Loop

Metric-Based

• ProtoNetsAlgorithm - Prototypical Networks
• MatchingNetworksAlgorithm
• RelationNetworkAlgorithm

Memory-Based

• MANNAlgorithm - Memory-Augmented NN
• NTMAlgorithm - Neural Turing Machine

Hybrid

• LEOAlgorithm - Latent Embedding Optimization
• CNAPAlgorithm - Conditional Neural Adaptive Processes
• TADAMAlgorithm - Task-Dependent Adaptive Metric
• MetaOptNetAlgorithm
• GNNMetaAlgorithm - Graph-based

Example:

var result = await new AiModelBuilder<float, Tensor<float>, Tensor<float>>()
    .ConfigureMetaLearning(new MAMLAlgorithm<float>(
        innerLearningRate: 0.01f,
        outerLearningRate: 0.001f,
        innerSteps: 5))
    .BuildAsync(metaTrainTasks);

// Few-shot learning on new task
var adapted = result.MetaLearner.Adapt(supportSet, numSteps: 10);
var predictions = adapted.Predict(querySet);

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Self-Supervised Learning (10+ Methods)

Click to expand SSL methods

Contrastive

• SimCLR
• MoCo - Momentum Contrast
• BYOL - Bootstrap Your Own Latent
• BarlowTwins
• SwAV - Swapping Assignments

Masked

• MAE - Masked Autoencoder
• BEiT

Distillation

• DINO - Self-Distillation
• iBOT

Evaluation

• Linear probing
• KNN evaluation
• Transfer benchmarks

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Optimizers (40+)

Click to expand all optimizers

Adaptive Learning Rate

• AdamOptimizer, AdamWOptimizer
• AdagradOptimizer
• AdaDeltaOptimizer
• AdaMaxOptimizer
• AMSGradOptimizer
• NadamOptimizer

Momentum-Based

• MomentumOptimizer
• NesterovAcceleratedGradientOptimizer

Second-Order

• BFGSOptimizer, LBFGSOptimizer
• NewtonMethodOptimizer
• LevenbergMarquardtOptimizer
• ConjugateGradientOptimizer

Large-Scale

• LAMBOptimizer - Layer-wise Adaptive
• LARSOptimizer - Layer-wise Rate Scaling
• LionOptimizer - Evolved Sign Momentum

Regularized

• FTRLOptimizer - Follow-The-Regularized-Leader

Evolutionary

• GeneticAlgorithmOptimizer
• ParticleSwarmOptimizer
• DifferentialEvolutionOptimizer
• CMAESOptimizer - Covariance Matrix Adaptation
• AntColonyOptimizer

Specialized

• BayesianOptimizer - Hyperparameter tuning
• NelderMeadOptimizer - Simplex method
• CoordinateDescentOptimizer
• ADMMOptimizer - Alternating Direction

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Loss Functions (37+)

• Classification: CrossEntropy, BinaryCrossEntropy, FocalLoss, LabelSmoothing
• Regression: MSE, MAE, Huber, LogCosh, Quantile
• Segmentation: Dice, IoU, Tversky, Lovász
• Metric Learning: Triplet, Contrastive, ArcFace, CosFace, Circle
• Generative: Wasserstein, Hinge, LSGAN
• Multi-task: Uncertainty-weighted, GradNorm

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Additional Features

AutoML

• Hyperparameter optimization (Bayesian, Random, Grid)
• Neural Architecture Search (NAS)
• Feature selection
• Model selection

Federated Learning

• FedAvg, FedProx, FedNova
• Secure aggregation
• Differential privacy
• Client selection strategies

Tokenization (HuggingFace Compatible)

• BPE, WordPiece, Unigram, SentencePiece
• Pre-trained tokenizer loading
• Custom tokenizer training

Model Compression

• Pruning (magnitude, structured)
• Quantization (INT8, FP16, INT4)
• Knowledge distillation
• Low-rank factorization

Uncertainty Quantification

• Monte Carlo Dropout
• Deep Ensembles
• Bayesian Neural Networks
• Conformal Prediction

Data Augmentation

• Image: Flip, rotate, crop, color jitter, MixUp, CutMix, AutoAugment
• Text: Synonym replacement, back-translation, EDA
• Audio: Time stretch, pitch shift, noise injection
• Tabular: SMOTE, ADASYN

Experiment Tracking

• Experiment management
• Metric logging
• Checkpoint management
• Model registry

Prompt Engineering

• Prompt templates
• Chain-of-thought
• Few-shot learning
• Prompt optimization

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Samples

See the samples/ directory for complete, runnable examples:

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API Reference

Full API documentation: docfx-generated reference at ooples.github.io/AiDotNet/api/; narrative docs and guides at aidotnet.dev/docs.

Key namespaces:

• AiDotNet - Core builder and result types
• AiDotNet.NeuralNetworks - All neural network architectures
• AiDotNet.Classification - Classification algorithms
• AiDotNet.Regression - Regression algorithms
• AiDotNet.Clustering - Clustering algorithms
• AiDotNet.ComputerVision - Vision models
• AiDotNet.Audio - Audio processing
• AiDotNet.ReinforcementLearning - RL agents
• AiDotNet.RetrievalAugmentedGeneration - RAG components
• AiDotNet.LoRA - Fine-tuning adapters
• AiDotNet.Diffusion - Diffusion models
• AiDotNet.DistributedTraining - Distributed strategies
• AiDotNet.MetaLearning - Meta-learning algorithms
• AiDotNet.TimeSeries - Time series models
• AiDotNet.Optimizers - Optimization algorithms

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Feature Maturity

The repository ships a wide surface; not every subsystem is at the same maturity level. The labels below set expectations for enterprise consumers — stable features are appropriate for production use today; beta features are functional but may have visible rough edges or API churn; experimental features are exploratory and should not be relied on in production; preview indicates work-in-progress that may not yet be feature-complete; stub indicates an interface present for forward compatibility but without a substantial implementation.

Duplicate type names. Thirty-three public type names are declared in two namespaces each — for example Donut, LayoutLMv3, StableVideoDiffusion, BarkModel, WaveNet. In all but two cases the two declarations are independent implementations rather than copies, and they often differ several-fold in size. Code that imports both namespaces cannot use the bare name until one side is qualified.

The labels reflect a static review of the public repository. They are informational only — production adopters should validate against their own threat model and SLA requirements.

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Platform Support

GPU Acceleration

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Contributing

We welcome contributions! See CONTRIBUTING.md for guidelines.

Areas where we especially welcome help:

• Additional model implementations
• Performance optimizations
• Documentation and examples
• Bug fixes and testing

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Community

• Issues: Report bugs or request features
• Discussions: Ask questions or share ideas
• Security: Report vulnerabilities

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License

Business Source License 1.1 — see LICENSE for full terms.

What's free (always, no license needed)

• Model training (all algorithms, all configurations)
• Model inference and prediction
• Data preprocessing, feature engineering, AutoML
• GPU acceleration on any hardware
• All utility functions and helpers

What requires a license

• Model save/load operations (SaveModel, LoadModel, Serialize, Deserialize)
• Free trial: 30 days or 10 operations, whichever comes first

License tiers

See the pricing page for details and to register for a free community license.

Each version automatically converts to Apache License 2.0 four years after release (per the BSL Change Date / fourth-anniversary clause in the LICENSE file).

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