Python machine learning applications in image processing, recommender system, matrix completion, netflix problem and algorithm implementations including Co-clustering, Funk SVD, SVD++, Non-negative Matrix Factorization, Koren Neighborhood Model, Koren Integrated Model, Dawid-Skene, Platt-Burges, Expectation Maximization, Factor Analysis, ISTA, FISTA, ADMM, Gaussian Mixture Model, OPTICS, DBSCAN, Random Forest, Decision Tree, Support Vector Machine, Independent Component Analysis, Latent Semantic Indexing, Principal Component Analysis, Singular Value Decomposition, K Nearest Neighbors, K Means, Naïve Bayes Mixture Model, Gaussian Discriminant Analysis, Newton Method, Coordinate Descent, Gradient Descent, Elastic Net Regression, Ridge Regression, Lasso Regression, Least Squares, Logistic Regression, Linear Regression

Built house price prediction model using linear regression and k nearest neighbors and used machine learning techniques like ridge, lasso, and gradient descent for optimization in Python

Self-contained implementation of non-convex optimization algorithms in Python. Steepest Descent, Conjugate Gradient, Newton's Method, Quasi-newton (BFGS), l-BFGS

An IPython notebook showing the basics of implementing gradient descent and stochastic gradient descent in Python

SelfTune is an RL framework that enables systems and service developers to automatically tune various configuration parameters and other heuristics in their codebase, rather than manually-tweaking, over time in deployment. It provides easy-to-use API (Python, C# bindings) and is driven by bandit-style RL & online gradient-descent algorithms.

A collection of various gradient descent algorithms implemented in Python from scratch

Parallel implementation of Stochastic Gradient Descent using SciKit-Learn library in Python.

Visualizing Gradient Descent with Momentum in Python

Implementation of multivariate linear regression using gradient descent in python

This package implements the Stochastic Gradient Descent algorithm in Python for Linear Regression, Ridge Regression, Logistic Regression and Logistic Regression with L2 Regularization.

This Repository contains Python Notebooks to learn PyTorch starting from the very basics of implementing gradient descent algorithm in Python and Numpy and slowly moving up the ladder to Deep Learning using PyTorch

Implement different variants of gradient descent in python using numpy

Implementation of Stochastic Gradient Descent algorithms in Python (cite https://doi.org/10.1007/s00158-020-02599-z)

This is a custom implementation of a logistic regression model in Python, created from scratch. The model uses gradient descent optimization to learn the optimal weights and bias for binary classification tasks. It also includes L2 regularization to prevent overfitting, with the regularization strength controlled by the lambda hyperparameter.

This program implements logistic regression from scratch using the gradient descent algorithm in Python to predict whether customers will purchase a new car based on their age and salary.

Hands on implementation of gradient descent based optimizers in raw python

In this project we will be using the publicly available and Kaggle-popular LendingClub data set to train Linear Regression and Extreme Gradient Descent Boosted Decision Tree models to predict interest rates assigned to loans. First, we will clean and prepare the data. This includes feature removal, feature engineering, and string processing.There are several entries where values have been deleted to simulate dirty data. Then, we will build machine learning models in Python to predict the interest rates assigned to loans. We will evaluate our models' performances using the root mean squared error (RMSE) metric and compare our models' results.

In recent years, speech emotion recognition is playing a vital role in today’s digital world. In our project, we considered RAVDESS Dataset for training the model. We considered 10 different Machine Learning Algorithms to find out which is the best algorithm among those by considering their accuracies. After that, we cleaned the dataset by applying mask function to remove unwanted background noise, so that we can increase the accuracy and again applied all 10 algorithms on this clean speech dataset to verify which is the best algorithm. Finally, by using that algorithm’s model, we tested a sample audio file to predict its emotion. KEYWORDS : Python, Librosa, Scikit-learn, Soundfile, Pyaudio, RAVDESS dataset, MLPClassifier, Logistic Regression, Naive Baye’s, K-Neighbor Classifier, XGB, LightGBM, Random Forest, Decision Tree, Stochastic Gradient Descent, Support Vector Machine, Jupyter Notebook.

Demonstrating solving Logistic regression using Gradient Descent or Newton Raphson optimisation with regularisation in Python.

This program implements linear regression from scratch using the gradient descent algorithm in Python. It predicts car prices based on selected features and uses a dataset of cars with their respective prices.

These are some popular Machine Learning Algorithms which I have implemented with some data sets. These include implementation of Linear Regression(multi variate), Gradient descent for Logistic and Linear Regression, Decision Trees, Random forests, Naive Bayes, KNN Implementation form scratch, SVM as a multiclass classifier, PCA implementation. They all are written in python 3.5 .

Linear Regression with gradient descent in pure python

Python feed-forward neural network to predict breast cancer. Trained using stochastic gradient descent in combination with backpropagation.

Implemented LinearRegression with SGD(Stochastic Gradient Descent) in python.

Ridge Regression in python, using gradient descent

Built classifiers using logistic regression and decision trees to classify product reviews and used machine learning techniques such as boosting, precision and recall, and stochastic gradient descent for optimization in Python

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A simple and easy to understand description of what gradient descent does with simple python code.

This tensorflow python program runs Logistic Regression Stochastic Gradient Descent Algorithm on the input dataset that is spread across 5 VMs in an asynchronous manner.

Simple Linear Regression with Gradient Descent implementation in Python