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This repository contains programming assignments and lecture notes from Andrew Ng's foundational deep learning course specialization on Coursera. The materials cover core neural network training techniques including optimization algorithms, normalization methods, regularization approaches, parameter initialization strategies, and learning rate scheduling to improve model convergence and generalization. The coursework explores design principles where successive neural network layers learn progressively more abstract feature representations from input data. It provides guidance on selecting ope

Vega-Lite is a high-level declarative language for specifying interactive, multi-view visualizations. It compiles a concise JSON specification into a full Vega visualization, automatically inferring scales, axes, and legends from encoding declarations. The grammar-of-graphics encoding maps data fields to visual channels such as position, color, size, and shape, while a multi-view composition grammar enables layered, faceted, concatenated, and repeated layouts. Reactive parameter binding links named parameters to input widgets, selections, and expressions for dynamic updates. The project suppo

DeepLearnToolbox is a research-oriented framework for constructing, training, and optimizing hierarchical neural networks within the Matlab and Octave environments. It provides a modular set of tools for building diverse network topologies, including feedforward, convolutional, and deep belief architectures, using native matrix-based numerical computation. The library distinguishes itself through its support for layer-wise unsupervised pre-training, which establishes initial weights for deep models before supervised fine-tuning. It incorporates stochastic gradient descent and backpropagation