Search Results for "exercise"

CourseraMachineLearning is a personal collection of resources, notes, and programming exercises from Andrew Ng’s popular Machine Learning course on Coursera. It consolidates lecture references, programming tutorials, test cases, and supporting materials into one repository for easier review and practice. The project highlights fundamental machine learning concepts such as hypothesis functions, cost functions, gradient descent, bias-variance tradeoffs, and regression models. It also organizes...

The collection MICROMECHANICS (micromechanics.zip) includes: (1) lecture notes (microbook.pdf) on the analysis of heterogeneous materials and homogenization, and (2) source codes (microcode.tar.bz2) that accompany the computational exercises in Part II of the notes. Instructions on using the codes are given in the README file of each exercise. Further instructions can be found in the file microcode.tar.bz2. The MATLAB codes are used for visualization, evaluating analytical bounds and estimates as well as in the generation of digital and particulate microstructures. The Fortran codes are based on the finite element method in linear and nonlinear settings, the latter capable of finite deformations with damage. ...

The Stanford Machine Learning Course Exercises repository contains programming assignments from the well-known Stanford Machine Learning online course. It includes implementations of a variety of fundamental algorithms using Python and MATLAB/Octave. The repository covers a broad set of topics such as linear regression, logistic regression, neural networks, clustering, support vector machines, and recommender systems. Each folder corresponds to a specific algorithm or concept, making it easy...