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This notebook demonstrates a neural network implementation using NumPy, without TensorFlow or PyTorch. Trained on the MNIST dataset, it features an architecture with input layer (784 neurons), two hidden layers (132 and 40 neurons), and an output layer (10 neurons) with sigmoid activation.
This code uses computational graph and neural network to solve the five-layer traffic demand estimation in Sioux Falls network. It also includes comparison of models and 10 cross-validations.
A gentle introduction to custom gradient propagation for ML application in which parameters of LTI systems have to be optimized. This example enables the integration of control theory with machine learning, for the development of Physical-Informed Neural Networks (PINNs)