There are 6 repositories under gradient-descent-algorithm topic.
MATLAB/Octave library for stochastic optimization algorithms: Version 1.0.20
A blog which talks about machine learning, deep learning algorithms and the Math. and Machine learning algorithms written from scratch.
Repository containing the colab-notebooks of the course i did on deep learning from One-Fourth-Labs
OnLine Low-rank Subspace tracking by TEnsor CP Decomposition in Matlab: Version 1.0.1
A collection of various gradient descent algorithms implemented in Python from scratch
[Python] [arXiv/cs] Paper "An Overview of Gradient Descent Optimization Algorithms" by Sebastian Ruder
My implementation of Batch, Stochastic & Mini-Batch Gradient Descent Algorithm using Python
MATLAB implementations of a variety of machine learning/signal processing algorithms.
D3.js | Visualization of the gradient descent algorithm for linear regression
Implementation of nonlinear Optimization Algorithms in Python
Implementation of Convex Optimization algorithms
Subsampled Riemannian trust-region (RTR) algorithms
An interesting application of gradient descent aims to stabilize any input graph such that the forces applied on each node are minimum and the overall model is stable. Visualization is perform using NetworkX.
:blue_book: Detailed Exploratory Data Analysis of Lending Club Loan Data
Predicting wine quality using regression on the well-known UCI data set and more
Identify which questions asked on Quora are duplicates of questions that have already been asked.This could be useful to instantly provide answers to questions that have already been answered,predict whether a pair of questions are duplicates or not
in this project we'll Implement the basic functions of the Gradient-Descent algorithm to find the boundary in a small dataset.
Linear Gradient Descent using C#
Simple linear regressor that tries to approximate a simple function deployed in Tensorflow 2.0 without Keras
A simple demonstration of gradient-descent algorithm in C++ on a linear funciton.
A Logistic model for predicting divorce rates among couples, implemented using the sklearn library.
Linear line fitting to data and optimising parameters with Gradient Descent algorithm
Utilizing Tensorflow's animation capabilities to visualize a Linear Regression model built for predicting House Prices.
Gradient Descent Algorithm Implementation using NumPy
Programming assignments completed for my Reinforcement Learning course: Topics include Bandit Algorithms, Dynamic Programming, policy iteration, Monte-Carlo methods, SARSA, Q-Learning, Dyna-Q/Dyna-Q+, gradient control methods, state aggregation methods, and Deep Q-Learning Networks (DQNs).
Predicting the number of comments on a post with Linear Regression, built from scratch.
Using language modelling to develop a text-completion application; studying interpolation of several n-gram models and opimisation of interpolation weights to minimise the perplexity of the model.
An overview of gradient descent optimization algorithms
Motion Planning and Navigation (Traversal using Gradient Descent Algorithm)
Gradient Descent for N features using two datasets: Boston House data, Power Plant Data
While it is convenient to use advanced libraries for day-to-day modeling, it does not give insight into the details of what really happens underneath, when we run the codes. In this work, we implement a logistic regression model manually from scratch, without using any advanced library, to understand how it works.
Solutions to Coursera's Intro to Machine Learning course in python