Pinned Repositories
2018-CS109A
Repository for CS109A Fall 2018
AWS_JB-NAT-FZ
Jumpbox + NAT instance + private machine in AWS
AWS_Jupyter-Installation-on-Ubuntu-Server
Set up Jupyter on Ubuntu server running on AWS EC2 instance and configure two Python3 virtual environments
Classifying-Songs-Genres-From-Audio-Data
Exploring a music dataset by examining correlations between numerical variables, running a principal component analysis for dimensionality reduction and finally fitting both scikit learn Decision Tree Classification and Logistic Regression models to compare their performance.
Cookbook
The Data Engineering Cookbook
databricks-spark-certification
Guide for databricks spark certification
Flask-API-deploying-a-model-into-production
This repository describes the steps taken to deploy a model trained and tested with scikit-learn python library. This model predicts if a given song is of type "Rock" or "Hip-Hop" based on certain features. Check link : https://github.com/femtonelson/Classifying-Songs-Genres-From-Audio-Data. This deployment is done with a web service development framework in Python known as Flask.
image-classification-AlexNet-vs-DenseNet-vs-ResNet-vs-From-Scratch
Compare various models, one from scratch(keras) and three pretrained : AlexNet, ResNet, DenseNet models (Pytorch) to choose the best model for classification.
Kaggle-titanic-survival-prediction
Prediction of survival using sklearn XGBoost
Time-series-power-consumption-forecasting
Using a SARIMA model to predict electrical power consumption
femtonelson's Repositories
femtonelson/Classifying-Songs-Genres-From-Audio-Data
Exploring a music dataset by examining correlations between numerical variables, running a principal component analysis for dimensionality reduction and finally fitting both scikit learn Decision Tree Classification and Logistic Regression models to compare their performance.
femtonelson/2018-CS109A
Repository for CS109A Fall 2018
femtonelson/AWS_JB-NAT-FZ
Jumpbox + NAT instance + private machine in AWS
femtonelson/AWS_Jupyter-Installation-on-Ubuntu-Server
Set up Jupyter on Ubuntu server running on AWS EC2 instance and configure two Python3 virtual environments
femtonelson/Cookbook
The Data Engineering Cookbook
femtonelson/databricks-spark-certification
Guide for databricks spark certification
femtonelson/Flask-API-deploying-a-model-into-production
This repository describes the steps taken to deploy a model trained and tested with scikit-learn python library. This model predicts if a given song is of type "Rock" or "Hip-Hop" based on certain features. Check link : https://github.com/femtonelson/Classifying-Songs-Genres-From-Audio-Data. This deployment is done with a web service development framework in Python known as Flask.
femtonelson/image-classification-AlexNet-vs-DenseNet-vs-ResNet-vs-From-Scratch
Compare various models, one from scratch(keras) and three pretrained : AlexNet, ResNet, DenseNet models (Pytorch) to choose the best model for classification.
femtonelson/Kaggle-titanic-survival-prediction
Prediction of survival using sklearn XGBoost
femtonelson/Time-series-power-consumption-forecasting
Using a SARIMA model to predict electrical power consumption
femtonelson/db-readings
Readings in Databases
femtonelson/DeepLearningBookCode-Volume1
Python/Jupyter notebooks for Volume 1 of "Deep Learning - From Basics to Practice" by Andrew Glassner
femtonelson/DLTFpT
Deep Learning with TensorFlow, Keras, and PyTorch
femtonelson/getting_started_with_pyspark
Materials for class Getting Started with Pyspark
femtonelson/jMetalPy
A framework for single/multi-objective optimization with metaheuristics
femtonelson/kale
Kubeflow’s superfood for Data Scientists
femtonelson/Leetcode-minpathsum
femtonelson/Linear-Regression-in-R
Predicting a startup's profitability with a linear regression model in R
femtonelson/Mayo-Clinic-Primary-Biliary-Cirrhos-Survival-Analysisis
Survival analysis of primary biliary cirrhosis patients
femtonelson/Metaheuristics
femtonelson/Mimic-SQL-Server-stored-procedure
Refresh a view when there are access restrictions to stored procedures/triggers
femtonelson/MNIST
Materials for the AWS Academy Course
femtonelson/Normal-assignment-shallowcopy-deepcopy
femtonelson/open-standard-models-2019
femtonelson/PySpark-on-Docker
femtonelson/rrcf
🌲 Implementation of the Robust Random Cut Forest algorithm for anomaly detection on streams
femtonelson/simanneal
Python module for Simulated Annealing optimization
femtonelson/test
femtonelson/waiting-time-in-a-clinic-FIFO-queue
This is a model of contention-based (without prior booking) patient consultation in a clinic. It allows the simulation of the waiting time of patients in a queue of type FIFO (First In First Out) using adjustable variables such as the initial patient count, the number of available waiting seats, the arrival rate of new patients and the rate at which the clinic consults patients. This could help clinics (or similar institutions) understand the impact of these variables on the waiting time of their patients (or clients).