gradientgmm: Fast Gaussian Mixtures in Scala
gradientgmm is a package for fast gaussian mixtures modelling in scala through stochastic gradient descent. In this post I show how it works.
1
Nestor Y. Sanchez
Mentor
(13 reviews)
US$20.00
For every 15 mins
25
Sessions/Jobs
ABOUT ME
Machine Learning Engineer
Machine Learning Engineer
Generalist with experience on all aspects of data science from formal academic research to product deployment; I have a strong background in mathematical statistics and the fundamentals of ML, as well as 3+ years of software engineering experience in industry helping companies create new products and insights from their data. I am also a self-learner always keeping up to date with innovations in the data science and MLOps spaces.
Spanish, English
Edinburgh (+00:00)
Joined October 2018
EXPERTISE
7 years experience
The projects I've worked on range from Finance to Marketing and Education. You can see some of them at www.nestorsag.com.
I usually work ...
The projects I've worked on range from Finance to Marketing and Education. You can see some of them at www.nestorsag.com.
I usually work with R, Python, Scala, MySQL, Linux and Git but I'm also comfortable with big data frameworks such as Spark, MongoDB and AWS/Google Cloud.
2 years experience
I've used Scala mainly to write Spark programs using Spark's MLlib; more recently, I used Scala and Spark for my MSc dissertation to dev...
I've used Scala mainly to write Spark programs using Spark's MLlib; more recently, I used Scala and Spark for my MSc dissertation to develop a library with an implementation of Gaussian Mixtures Model amenable for stochastic optimisation and streaming data. You can find it in https://github.com/nestorSag/streaming-gmm
6 years experience | 2 endorsements
I'm a very experienced R user; some of the stuff I've done with it includes fitting Machine Learning models, Text Mining, network modelli...
I'm a very experienced R user; some of the stuff I've done with it includes fitting Machine Learning models, Text Mining, network modelling and analysis, data visualisation, web scrapping, and huge amounts of data cleaning.
6 years experience | 12 endorsements
I've used Python for 4 years now, mainly to do data cleaning and to deploy small web applications on Google Cloud using Flask; more recen...
I've used Python for 4 years now, mainly to do data cleaning and to deploy small web applications on Google Cloud using Flask; more recently I've used it to develop some Machine Learning models using Pytorch and sklearn.
7 years experience | 1 endorsement
My Applied Mathematics background has a heavy Statistics component that I expanded in the MSc and plan to keep expandin on the PhD; this ...
My Applied Mathematics background has a heavy Statistics component that I expanded in the MSc and plan to keep expandin on the PhD; this includes modelling approaches that might be very useful with data that have special structure and that vanilla Machine Learning models can't directly handle, like Generalized Linear Models for ordinal and integer outcomes, or time series models for sequential data; non-parametric approaches that allows to fit highly non-linear models with very few observations. Using statistical theory along with other tools generally enhances interpretability and model accuracy.
6 years experience | 2 endorsements
2 years experience
REVIEWS FROM CLIENTS
(13 reviews)
Hernan Trujillo
September 2022
The best in Python!
Hernan Trujillo
September 2022
The best in python! highly recommended !!!
Vanessa Orozco
May 2022
Improved my method which was a bit flawed. He took the time to explain, show step by step and debug with good humor and fighting spirit. I would love to recommend this mentor!
SOCIAL PRESENCE
streaming-gmm
This repository contains my MSc dissertation project. Iti s an implementation of a streaming GMM algorithm in Spark.
Scala
7
2
rlmodels
a small reinforcement learning library with implementations for DDPG, double Q networks, A2C and CMAES.
Python
1
2
380 Reputation
0
2
13
EMPLOYMENTS
Statistical consultant
National Grid
2021-03-01-2021-05-01
Working with National Grid and other academic consultants, delivered a study on projected mid-term security of supply indices for the UK ...
Working with National Grid and other academic consultants, delivered a study on projected mid-term security of supply indices for the UK power system in a range of decarbonisation scenarios
R
Statistics
Data Science
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R
Statistics
Data Science
Data Visualization
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ML consultant
Wella School Systems
2018-09-01-2018-11-01
developed an end-to-end machine learning pipeline for student failure prediction intended as a new data service
developed an end-to-end machine learning pipeline for student failure prediction intended as a new data service
Python
MongoDB
Machine learning
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Python
MongoDB
Machine learning
Statistics
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Lead Data Scientist
Sinnia
2015-07-01-2017-08-01
* Created data modelling and visualisation products on top of the Twitter Firehose to forecast the reach of influencer-based marketing ca...
* Created data modelling and visualisation products on top of the Twitter Firehose to forecast the reach of influencer-based marketing campaigns on different consumer segments.
* Helped expand the company’s catalogue of data analytics services through the development of machine learning pipelines for the inference of sociodemographic features of social media users.
* Used NLP techniques and network models on a regular basis to answer customer-specific analytics questions regarding public perception and conversation on social media for customers like Coca-Cola and the Mexican Presidential Office.
* Implemented network community detection algorithms from the academic literature to improve the catalogue of analytics services
Python
Java
Scala
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Python
Java
Scala
MySQL
Git
Linux
R
Bash
Google Cloud Platform
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PROJECTS
Fitting Large-Scale Gaussian Mixtures With Accelerated Gradient DescentView Project
2018
A Gaussian Mixture (GM) is a popular clustering model that is usually fitted using the Expectation-Maximization (EM) algorithm. This mak...
A Gaussian Mixture (GM) is a popular clustering model that is usually fitted using the Expectation-Maximization (EM) algorithm. This makes the model difficult to scale since EM is a batch algorithm, not suited for very large datasets or data streams. Taking this paper as starting point, in this project I developed a Scala library that implements accelerated stochastic gradient descent GMMs, which solves the problems mentioned above and achieves fast convergence; it can run sequentially or in parallel using Spark.
Scala
Machine learning
Mathematics
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Scala
Machine learning
Mathematics
Apache Spark
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Generative deep learning models for text font generationView Project
2021
Python package implementing a GCP-based end-to-end machine learning pipeline for generative deep learning models using typeface data. It ...
Python package implementing a GCP-based end-to-end machine learning pipeline for generative deep learning models using typeface data. It uses Beam for data preprocessing, Tensorflow for model training and MLFlow for experiment tracking.
Python
Docker
Google Cloud Platform
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Python
Docker
Google Cloud Platform
TensorFlow
Apache Beam
MLflow
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