This repository is an assignment I completed for CMPUT466. The goal is to compare different machine learning algorithms on a real task.
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Updated
Nov 19, 2020 - Jupyter Notebook
This repository is an assignment I completed for CMPUT466. The goal is to compare different machine learning algorithms on a real task.
Detect fraudulent credit card transactions through supervised machine learning
Automatically create a config of hyper-parameters from global variables
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.
Some of experiences in Machine Learning field
Sweep through ranges of command line hyperparameters to create testcases for multiple corners
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
Github repo for ML Specialization course on Coursera. Contains notes and practice python notebooks.
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.
Bayesian optimization using Gaussian Process regression (Python)
Gnarl - An easy to use Deep Learning framework for Python
Hyperparameter search algorithm
As the learning rate is one of the most important hyper-parameters to tune for training convolutional neural networks. In this paper, a powerful technique to select a range of learning rates for a neural network that named cyclical learning rate was implemented with two different skewness degrees. It is an approach to adjust where the value is c…
A simple web app that helped students visualize the SVM algorithm according to their choice of hyperparameter setting.
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