Model search in traditional machine learning algorithms (non DL) and DL starter codes on MNIST dataset. This is a good starter code for beginners trying to learn about curse of dimensionality, overfitting and other concepts in general

Machine learning with sklearn

Reducing overfitting in perdiction in decision trees

I had created this in the year 2012, when I was doing my 10th standard schooling. This program uses difference equation concept to predict the next value in a polynomial series.

Curso Convolutional Neural Networks in TensorFlow disponibilizado pela DeepLearning.AI e ministrado através do Coursera

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

Supplementary code for the paper "Stochastic Weight Matrix-based Regularization Methods for Deep Neural Networks" - an accepted paper of LOD2019

Códigos e enunciados utilizados no curso de Aprendizado de Máquina feito em 2020.

Udacity Self Driving Car Nanodegree - Behavioral Cloning

spatial resampling for more robust cross validation in spatial studies

A study of the problem of overfitting in deep neural networks, how it can be detected, and prevented using the EMNIST dataset. This was done by performing experiments with depth and width, dropout, L1 & L2 regularization, and Maxout networks.

Clipped Noise Softmax to overcome over-fitting with Softmax - PyTorch implementation

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

Simple attempt to create neural network head that would prevent overfitting

This repository contains the notes, codes, assignments, quizzes and other additional materials about the course "AI for Medical Prognosis" from DeepLearning.AI Coursera.

Assignments in unconstrained optimization course covering 1st half of Nocedal and Wright textbook.

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.

Unknown function approximation, given input-output measurements, using Genetic Algorithm

Pytorch DataLoader wrapper to intentionally mess up, corrupt, shuffle, randomize the input/label correspondence.