Overview

h4ks's neural module is a lightweight, adaptive neural network framework designed for rapid prototyping and deployment of AI models. It emphasizes modularity, allowing seamless integration with existing systems.

Key Features

• Dynamic architecture adaptation: Automatically adjusts layer configurations based on input complexity.
• Modular design: Supports plug-and-play components for custom workflows.
• Real-time learning: Enables on-the-fly model retraining with new data streams.
• Cross-platform compatibility: Works with TensorFlow, PyTorch, and ONNX formats.
• Security enhancements: Built-in adversarial attack detection and mitigation.

Technical Specifications

• Layers: Input, hidden (fully connected/convolutional), and output layers.
• Activation functions: ReLU, Sigmoid, Tanh, and custom variants.
• Training algorithms: Adam, SGD, and hybrid optimizers.
• Optimization techniques: Batch normalization, dropout, and quantization.

Use Cases

Machine Learning

• Rapid model iteration for classification and regression tasks.
• Example:

<syntaxhighlight lang="python"> model = NeuralModule(input_size=784, output_size=10) model.add_layer(128, activation='relu') model.train(data, labels) </syntaxhighlight>

Cybersecurity

• Anomaly detection in network traffic or user behavior.

Robotics

• Real-time sensor data processing for autonomous decision-making.

Healthcare

• Predictive analytics for patient diagnostics using structured/semi-structured data.

Implementation Details

• Written in C++ with Python bindings.
• Dependencies:
• TensorFlow Lite
• OpenCV (for image processing)
• License: MIT (open-source)

References

<references> <ref name="h4ks_github">GitHub Repository</ref> <ref name="paper">Adaptive Neural Architectures, 2023</ref> </references>