Dropout in Deep Learning

Fraud detection over twitter feed data

Python version of Andrew Ng's Machine Learning Course.

A series of documented Jupyter notebooks implementing polynomial regression models and model performance analysis

Adding noise as regularization method to reduce overffiting in neural networks

A visual example of the concepts of under and overfitting in supervised machine learning using U.S. state border data.

Implementation of Decision Tree and Random Forest algorithms, with various hyperparameters, developed from scratch and using scikit-learn for comparison and analysis.

Evaluating classifier using Python focus on evaluation metrics and hyperparameter turning

Xinshao Wang, Ex-Postdoc and Ex-Visit Scholar@University of Oxford, Ex-Senior Researcher@ZenithAI

Overfitting and Underfitting in Machine Learning

Brief study on Underfitting and Overfitting in Machine Learning

Built a model to predict the value of a given house in the Boston real estate market using various statistical analysis tools. Identified the best price that a client can sell their house utilizing machine learning.

Supervised Learning - Regression Algorithm

IMP KEYS OF ML MODEL

In this repository you will learn how to handle overfitting with the help of Lasso and Ridge Regression regularizations, also working mechanism of those while using useful charts.

Make use of PyTorch's custom modules to define a network architecture and train a model. Investigate how to improve a model's performance and deploy your model for wider use.

This repository provides a series of interactive Jupyter Notebook exercises designed to teach fundamental deep learning concepts through hands-on implementation and experimentation.

Content: Classification, Sigmoid function, Decision Boundary, Cost function, Gradient descent, Overfitting, Regularisation

This project helps exercising my machine learning modelling skills and evaluation techniques on image-based models.

A repository documenting the work for the Special Course with Professor Søren Hauberg from DTU Cognitive Systems.