This repository is an assignment I completed for CMPUT466. The goal is to compare different machine learning algorithms on a real task.

Automatically create a config of hyper-parameters from global variables

Detect fraudulent credit card transactions through supervised machine learning

textRec utlizes Latent Dirichlet Allocation and Jensen-Shannon-Divergence on the discrete probability distributions over LDA topics per document, in order to recommend unique and novel documents to specific users.

Simple logging wrapper for model hyperparameters from gensim.d2v, sklearn and keras.

This project is part of the Udacity Azure ML Nanodegree. In this project, we build and optimize an Azure ML pipeline using the Python SDK and a provided Scikit-learn model. This model is then compared to an Azure AutoML run.

Detect risk of critical temperature in materials, through supervised machine learning, testing 6 classification models and 6 regression models (w/CV) and their hyperparameters

Some of experiences in Machine Learning field

Sweep through ranges of command line hyperparameters to create testcases for multiple corners

bagging and hyperparameter tuning on spam vs not spam dataset

theory and basic research about pros and cons of ML with Python, with BigQuery and Google AutoML. The results worth taking a glance.

Computer vision pipeline for face emotion recognition.

Bayesian optimization using Gaussian Process regression (Python)

Deep Learning concepts and techniques: Regularization, Epochs, Batch,Hyperparameters, Cross validation, Optimizers

Example of Neu.ro integration with NNI for hyperparameter tuning

Bachelor Degree Project in Information Technology

Comprehensive image classification for training multilayer perceptron (MLP), LeNet, LeNet5, conv2, conv4, conv6, VGG11, VGG13, VGG16, VGG19 with batch normalization, ResNet18, ResNet34, ResNet50, MobilNetV2 on MNIST, CIFAR10, CIFAR100, and ImageNet1K.

A Hyperparameter Tuning algorithm.

Housing Price Prediction -Advanced regression techniques