❯ VanceNet is a neural network system that combines evolutionary principles with dynamic neural behavior. It aims to generate complex patterns by simulating the interaction of multiple layers of neurons. Through a series of training steps, the network learns to produce and evolve emergent patterns, which are then analyzed based on their entropy, fractal dimension, and complexity.
This system is designed with flexibility and scalability in mind, providing users with tools for neural network training, pattern storage, and performance analysis. The network's evolutionary features allow it to adapt and improve over time, making it suitable for tasks involving pattern recognition, generative modeling, and complex systems simulation.
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VanceNet is a neural network that evolves patterns through mutation-based learning. Neurons adjust their weights and biases, generating complex outputs. The network evaluates these outputs based on entropy, fractal dimension, and complexity, refining patterns over time. It is designed for pattern recognition and complexity analysis.
doudol/VanceNet-Research
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VanceNet is a neural network that evolves patterns through mutation-based learning. Neurons adjust their weights and biases, generating complex outputs. The network evaluates these outputs based on entropy, fractal dimension, and complexity, refining patterns over time. It is designed for pattern recognition and complexity analysis.
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