Kulbear/deep-learning-courseraArchived
Deep Learning Coursera
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 open-source, community-driven deep learning frameworks that minimize code complexity and development effort.
The training methodology encompasses comprehensive optimization algorithms, normalization techniques, regularization methods, hyperparameter tuning strategies, data splitting approaches, and augmentation techniques to maximize model performance and generalization. Topics include Adam optimization combining momentum with adaptive learning rates, batch normalization for stabilizing training, L2 regularization for reducing overfitting, and various hyperparameter search strategies such as random search and logarithmic sampling.
