# Neurenix

## Docs

- [AutoML](https://mintlify.wiki/MilesONerd/neurenix/advanced/automl.md): Automated hyperparameter optimization and neural architecture search
- [Continual Learning](https://mintlify.wiki/MilesONerd/neurenix/advanced/continual-learning.md): Prevent catastrophic forgetting with EWC, replay buffers, and regularization
- [Distributed Training](https://mintlify.wiki/MilesONerd/neurenix/advanced/distributed-training.md): Train models across multiple GPUs and nodes with MPI, Horovod, and DeepSpeed
- [Federated Learning](https://mintlify.wiki/MilesONerd/neurenix/advanced/federated-learning.md): Train models across distributed clients while preserving data privacy
- [Model Quantization](https://mintlify.wiki/MilesONerd/neurenix/advanced/quantization.md): Reduce model size and accelerate inference with INT8, FP16, and FP8 quantization
- [Environments](https://mintlify.wiki/MilesONerd/neurenix/agents/environments.md): Create custom environments for agents to interact with in the Neurenix framework
- [Multi-Agent Systems](https://mintlify.wiki/MilesONerd/neurenix/agents/multi-agent.md): Build and coordinate multiple agents in shared environments with Neurenix's MultiAgent system
- [Agents Overview](https://mintlify.wiki/MilesONerd/neurenix/agents/overview.md): Learn about the agent-based AI system in Neurenix for building intelligent agents and multi-agent systems
- [Single Agent](https://mintlify.wiki/MilesONerd/neurenix/agents/single-agent.md): Create and customize individual agents in Neurenix for reinforcement learning and autonomous behavior
- [Agent](https://mintlify.wiki/MilesONerd/neurenix/api/agent.md): Base class for AI agents
- [Data Loaders](https://mintlify.wiki/MilesONerd/neurenix/api/data-loaders.md): Efficient data loading and batching utilities
- [DatasetHub](https://mintlify.wiki/MilesONerd/neurenix/api/dataset-hub.md): Easy dataset loading and management
- [Device](https://mintlify.wiki/MilesONerd/neurenix/api/device.md): Hardware device abstraction for tensor operations
- [Module](https://mintlify.wiki/MilesONerd/neurenix/api/module.md): Base class for neural network modules
- [MultiAgent](https://mintlify.wiki/MilesONerd/neurenix/api/multi-agent.md): Multi-agent system for coordinating multiple agents
- [Activation Functions](https://mintlify.wiki/MilesONerd/neurenix/api/nn/activations.md): Non-linear activation functions for neural networks
- [Neural Network Layers](https://mintlify.wiki/MilesONerd/neurenix/api/nn/layers.md): Core neural network layer implementations
- [Loss Functions](https://mintlify.wiki/MilesONerd/neurenix/api/nn/loss-functions.md): Loss functions for training neural networks
- [Optimizers](https://mintlify.wiki/MilesONerd/neurenix/api/nn/optimizers.md): Optimization algorithms for training neural networks
- [RL Algorithms](https://mintlify.wiki/MilesONerd/neurenix/api/rl-algorithms.md): Reinforcement learning algorithm implementations
- [Tensor](https://mintlify.wiki/MilesONerd/neurenix/api/tensor.md): Multi-dimensional array with hardware acceleration support
- [Utilities](https://mintlify.wiki/MilesONerd/neurenix/api/utils.md): Utility functions and helper classes
- [neurenix dataset](https://mintlify.wiki/MilesONerd/neurenix/cli/dataset.md): Manage datasets including downloading, registering, splitting, and converting
- [neurenix eval](https://mintlify.wiki/MilesONerd/neurenix/cli/eval.md): Evaluate a trained model with specific metrics
- [neurenix export](https://mintlify.wiki/MilesONerd/neurenix/cli/export.md): Export a trained model to various formats for deployment
- [neurenix hardware](https://mintlify.wiki/MilesONerd/neurenix/cli/hardware.md): Manage hardware settings including device selection and benchmarking
- [neurenix help](https://mintlify.wiki/MilesONerd/neurenix/cli/help.md): Display help information about CLI commands and usage
- [neurenix init](https://mintlify.wiki/MilesONerd/neurenix/cli/init.md): Initialize a new Neurenix project with folder structure and configuration
- [neurenix monitor](https://mintlify.wiki/MilesONerd/neurenix/cli/monitor.md): Monitor model training in real-time with metrics tracking and visualization
- [neurenix optimize](https://mintlify.wiki/MilesONerd/neurenix/cli/optimize.md): Optimize models using quantization, pruning, distillation, and hyperparameter tuning
- [CLI Overview](https://mintlify.wiki/MilesONerd/neurenix/cli/overview.md): Command-line interface for the Neurenix AI framework
- [neurenix predict](https://mintlify.wiki/MilesONerd/neurenix/cli/predict.md): Make predictions using a trained model on new data
- [neurenix preprocess](https://mintlify.wiki/MilesONerd/neurenix/cli/preprocess.md): Preprocess input data for model training with various transformations
- [neurenix run](https://mintlify.wiki/MilesONerd/neurenix/cli/run.md): Run a model training script with specified configuration options
- [neurenix save](https://mintlify.wiki/MilesONerd/neurenix/cli/save.md): Save the current project state including models, configurations, and data
- [neurenix serve](https://mintlify.wiki/MilesONerd/neurenix/cli/serve.md): Serve a trained model as a RESTful API for production deployment
- [Training Models](https://mintlify.wiki/MilesONerd/neurenix/cli/train.md): Train models using the neurenix run command
- [Architecture Overview](https://mintlify.wiki/MilesONerd/neurenix/core/architecture.md): Understanding Neurenix's hot-swappable backend architecture and Genesis system
- [Devices](https://mintlify.wiki/MilesONerd/neurenix/core/devices.md): Hardware abstraction layer for CPU, GPU, TPU, and specialized accelerators
- [Neural Networks](https://mintlify.wiki/MilesONerd/neurenix/core/neural-networks.md): Building and training neural networks with Neurenix's modular architecture
- [Tensors](https://mintlify.wiki/MilesONerd/neurenix/core/tensors.md): Multi-dimensional arrays with automatic device management and hardware acceleration
- [API Model Serving](https://mintlify.wiki/MilesONerd/neurenix/deployment/api-serving.md): Serve Neurenix models via REST, WebSocket, and gRPC APIs
- [Docker Containerization](https://mintlify.wiki/MilesONerd/neurenix/deployment/docker.md): Package and deploy Neurenix models using Docker containers
- [Edge Device Deployment](https://mintlify.wiki/MilesONerd/neurenix/deployment/edge-devices.md): Optimize and deploy Neurenix models on edge devices and resource-constrained environments
- [Kubernetes Deployment](https://mintlify.wiki/MilesONerd/neurenix/deployment/kubernetes.md): Deploy and scale Neurenix models on Kubernetes clusters
- [ONNX Model Export and Import](https://mintlify.wiki/MilesONerd/neurenix/deployment/onnx.md): Convert Neurenix models to and from ONNX format for framework interoperability
- [ARM Acceleration](https://mintlify.wiki/MilesONerd/neurenix/hardware/arm.md): ARM processor acceleration with NEON, SVE, and Ethos-U NPU support
- [CUDA Support](https://mintlify.wiki/MilesONerd/neurenix/hardware/cuda.md): NVIDIA GPU acceleration with CUDA and Tensor Cores in Neurenix
- [FPGA Support](https://mintlify.wiki/MilesONerd/neurenix/hardware/fpga.md): FPGA acceleration with OpenCL, Xilinx Vitis, and Intel OpenVINO
- [NPU Support](https://mintlify.wiki/MilesONerd/neurenix/hardware/npu.md): Neural Processing Unit acceleration for edge AI inference in Neurenix
- [Hardware Acceleration Overview](https://mintlify.wiki/MilesONerd/neurenix/hardware/overview.md): Comprehensive hardware acceleration support in Neurenix AI framework
- [ROCm Support](https://mintlify.wiki/MilesONerd/neurenix/hardware/rocm.md): AMD GPU acceleration with ROCm/HIP in Neurenix AI framework
- [Installation](https://mintlify.wiki/MilesONerd/neurenix/installation.md): Install Neurenix using pip, conda, or from source
- [Introduction to Neurenix](https://mintlify.wiki/MilesONerd/neurenix/introduction.md): Learn about Neurenix, the AI framework optimized for Edge AI, multi-agent systems, and distributed computing
- [Explainable AI](https://mintlify.wiki/MilesONerd/neurenix/modules/explainability.md): Tools for interpreting and explaining model predictions
- [Fuzzy Logic Systems](https://mintlify.wiki/MilesONerd/neurenix/modules/fuzzy-logic.md): Fuzzy logic inference systems for handling uncertainty and vagueness
- [Graph Neural Networks (GNN)](https://mintlify.wiki/MilesONerd/neurenix/modules/gnn.md): Graph Neural Networks for processing graph-structured data
- [Neuro-Symbolic AI](https://mintlify.wiki/MilesONerd/neurenix/modules/neuro-symbolic.md): Integration of neural networks with symbolic reasoning
- [Neuroevolution](https://mintlify.wiki/MilesONerd/neurenix/modules/neuroevolution.md): Evolutionary algorithms for neural network optimization
- [Quantum Computing](https://mintlify.wiki/MilesONerd/neurenix/modules/quantum.md): Quantum computing integration with Qiskit and Cirq
- [Quickstart](https://mintlify.wiki/MilesONerd/neurenix/quickstart.md): Build your first AI model with Neurenix in 5 minutes
- [Algorithms](https://mintlify.wiki/MilesONerd/neurenix/rl/algorithms.md): Deep reinforcement learning algorithms in Neurenix
- [Reinforcement Learning](https://mintlify.wiki/MilesONerd/neurenix/rl/overview.md): Train intelligent agents with Neurenix RL module
- [Policies](https://mintlify.wiki/MilesONerd/neurenix/rl/policies.md): Action selection strategies for reinforcement learning agents
- [Training](https://mintlify.wiki/MilesONerd/neurenix/rl/training.md): Train and optimize reinforcement learning agents