This tutorial teaches how to design, train, and implement neural network decision frameworks using Exiasi's simulation API.
Exiasi's AI systems combine layered neural networks with real-time pattern recognition to create intelligent decision frameworks.
This tutorial assumes basic knowledge of machine learning concepts and Python.
Exiasi frameworks use multi-layered perceptrons with adaptive activation functions.
# Sample neural network initialization
network = ExiaNetwork(
layers=[64, 32, 16, 1],
activation='adaptive_relu',
learning_rate=0.07
)
Accepts raw data inputs for processing
Adaptive neurons that learn pattern recognition
Our systems use gradient-descent optimization with real-time weight adjustment.
# Sample training session
training_data = [
[0.1, 0.7, 0.3],
[0.9, 0.4, 0.6]
]
network.train(data=training_data, epochs=200)
Iterative optimization through backpropagation
This demo trains a network to identify patterns in sequential data. Use the Exiasi API to create your own pattern recognition system.
Our frameworks allows for adaptive learning rates to optimize convergence in complex environments.
Simultaneously process diverse input types (text, images, sensor data) with context-aware normalization.
Ready to take your AI skills further? Try our interactive demo or explore other tutorials in our training path.