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Table of Contents Article Overview Framework Workflow Code Analysis Article Overview Problem Context: Hyperspectral compressive imaging utilizes compressed sensing theory to capture hyperspectral data through snapshot measurements via coded apertures, avoiding temporal scanning. The core challenge lies in reconstructing the original hypers ...

Neural network vectorization refers to converting input data into vector form to facilitate processing by neural networks. The benefits include: Improved computational efficiency: Vectorized inputs enable praallel computation, accelerating training and inference. Reduced storage: Compresssing raw data into smaller vectors reduces memory usage. ...

Cost Function for Neural Networks The cost function for a neural network extends the logistic regression cost to handle multiple output units. Define: (L): total number of layers (s_l): number of units (excluding bias) in layer (l) (K): number of output units (classes) For a binary classification (K=1), the hypothesis (h_\Theta(x)) is a scala ...

Synthetic Training Data Generation import torch import torch.nn.functional as F import matplotlib.pyplot as plt import numpy as np # Create synthetic dataset train_x = torch.linspace(-1, 1, 100).view(-1, 1) train_y = train_x ** 2 + 0.2 * torch.rand(train_x.size()) # Visualize input-output distribution plt.scatter(train_x.numpy(), train_y.nump ...