From-scratch NumPy implementations of Perceptron and Multi-Layer Perceptron (MLP) for deep learning coursework, with experiments on Gaussian datasets, make_moons, and batch size analysis for BGD/SGD.

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Abstract: This paper discusses optimal batch-to-batch (B2B) control problems and presents a gradient descent method solution for unknown linear batch process systems. Using historical process data, we ...

A fortnight ago, in the dying days of the Trump administration, the Office of the United States Trade Representative wrote a letter to Labor senator Alex Gallacher — who was chairing the forthcoming ...

Abstract: A new on-board turbo-fan engine modeling method based on a batch normalize (BN) mini-batch gradient descent (MGD) deep neural network (NN) is proposed. This new method adopts BN algorithm, ...

Stochastic gradient descent (SGD) is pivotal in solving optimisation problems within deep learning. SGD utilises random subsets of data to compute gradients, enhancing its effectiveness for non-convex ...