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

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

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

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

Supervised Learning - Regression Algorithm

Brief study on Underfitting and Overfitting in Machine Learning

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.

IMP KEYS OF ML MODEL

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.

Overfitting and Underfitting in Machine Learning

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

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

Overfitting is often caused by using a model with too many parameters or if the model is too powerful for the given dataset. On the other hand, underfitting is often caused by the model with too few parameters or by using a model that is not powerful enough for the given dataset. In this we are discussing about that.

Recognize underfitting and overfitting, implement bagging and boosting, and build a stacked ensemble model using a number of classifiers.

Pursued an Introductory Machine Learning course in Python on Kaggle in my free time, where I practiced on a dataset and built a small model on Kaggle.