batch-gradient-descent

[Python] [arXiv/cs] Paper "An Overview of Gradient Descent Optimization Algorithms" by Sebastian Ruder

Compilation of different ML algorithms implemented from scratch (and optimized extensively) for the courses COL774: Machine Learning (Spring 2020) & COL772: Natural Language Processing (Fall 2020)

线性回归算法，close-form, batch 梯度下降，mini-batch 梯度下降，随机梯度下降，RMSE

PyTorch Implementation of Optimizers for Deep Learning from scratch.

Batch GD & Stochastic GD built from scratch using only NumPy — not to chase a high R² score, but to understand how a model actually learns. Gradients derived manually from MSE loss, weights updated step by step, convergence visualized live with animated charts.

Implementation of linear regression with L2 regularization (ridge regression) using numpy.

Following and implementing (some of) the machine learning algorithms from scratch based on the Stanford CS229 course.

Linear Regression - Batch Gradient Descent

⚛️ Experimenting with three different algorithms to train linear regression models

Gradient Descent with multiple method: Univariate - Multivariate, Momentum, Batch Gradient Descent, ...

A basic neural net built from scratch.

Recreated Poudlard's Sorting Hat by implementing logistic regression from scratch.

Multivariate linear regression implemented from scratch using vectorized gradient descent in NumPy. Includes manual cost computation, gradient derivation, feature scaling, and convergence analysis.