Learn machine learning from the ground up - using Python and a handful of fundamental tools. This repository contains a range of resources associated with the 2nd edition of the university textbook ...

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.

Hint: For regression, we use linear activations for output neurons. An MLP is demonstrated in the following figure: An MLP is composed of one input layer, one output layer, and one or more hidden ...

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A central characteristic of Bayesian statistics is the ability to consistently incorporate prior knowledge into various modeling processes. In this paper, we focus on translating domain expert ...

Quantitative dynamic models are widely used to study cellular signal processing. A critical step in modelling is the estimation of unknown model parameters from experimental data. As model sizes and ...

Experimental studies support the notion of spike-based neuronal information processing in the brain, with neural circuits exhibiting a wide range of temporally-based coding strategies to rapidly and ...

Crop yield is a highly complex trait determined by multiple factors such as genotype, environment, and their interactions. Accurate yield prediction requires fundamental understanding of the ...