# Moku Neural Network
The Moku Neural Network enables the deployment of real-time deep learning algorithms directly within your test instruments. Unlike traditional machine learning models that require extensive training and specialized knowledge, the Moku Neural Network offers a flexible, FPGA-based architecture designed for low-latency inference. This allows for efficient, real-time processing without the long training times typically associated with neural networks.
The Moku Neural Network is optimized for tasks like:
- Closed-loop control
- Noise filtering
- Signal classification
- Accuracy detection
- Quadrant sensor control
# Neural Network Applications
Here are some practical applications where the Moku Neural Network can be used:
- Signal Classification: Helps in identifying patterns in noisy signals, making real-time data processing more accurate and reliable.
- Quadrant Photodiode Sensing: Enhances your photodiode-based systems for accurate light detection, essential for various optical setups.
- Building a Neural Network: You can construct a neural network using Python, and then deploy it directly to the Moku platform.