Eliot Andres, Developer in Paris, France
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Eliot Andres

Verified Expert  in Engineering

Computer Vision Developer

Location
Paris, France
Toptal Member Since
July 26, 2018

Eliot is a capable young engineer specializing in machine learning and deep learning. He helps his clients create functional prototypes and optimize existing models. With a solid academic foundation and a couple years of professional experience under his belt, he is sure to be an asset to any project.

TensorFlowComputer VisionPython 3KerasAndroidAgileiOSLinuxGitPythonApache KafkaDlibNode.jsImpalaJavaScript 6

Portfolio

Freelance
Core ML, XGBoost, Dlib, Scikit-learn, TensorFlow
Linkfluence
Apache Kafka, Keras, TensorFlow
Mbr Targeting
Impala, Aerospike, Node.js, Apache Kafka

Experience

TensorFlow - 3 yearsXGBoost - 2 yearsScikit-learn - 2 yearsKeras - 2 yearsComputer Vision - 2 yearsiOS - 2 yearsML Kit - 1 year

Availability

Part-time

Preferred Environment

Python, Git, Linux

The most amazing...

...project I've contributed to involved processing 100+ million images/month with deep learning.

Work Experience

Freelance Machine Learning Engineer

2017 - PRESENT
Freelance
  • Developed face emotion detection, retraining face landmarks and migrating to iOS.
  • Ported models (SVM, CNN) to CoreML on iOS.
  • Made sales predictions using XGBoost for a national bank.
  • Developed retinopathy detection using convolutional neural networks.
  • Built quality insurance on a production line on Android devices using deep learning.
  • Developed real-time semantic image search using word vectors and CNNs.
Technologies: Core ML, XGBoost, Dlib, Scikit-learn, TensorFlow

Deep Learning Engineer

2017 - 2017
Linkfluence
  • Developed a logo detection algorithm using the latest deep learning architectures (Bi-LSTM, CNN, attention networks).
  • Set up the infrastructure to apply this algorithm in production. Performance: 100+ million images per month.
Technologies: Apache Kafka, Keras, TensorFlow

Back-end Engineer

2016 - 2016
Mbr Targeting
  • Enhanced the Node.js stack, handling more than 25 billion queries/month.
  • Made extensive use of Redis, Kafka, ZeroMQ, and Aerospike.
  • Implemented Node profiling and monitoring.
  • Improved deployment procedure using Puppet, Nagios, and Cyanite.
Technologies: Impala, Aerospike, Node.js, Apache Kafka

Pretrained.ml

http://pretrained.ml/
List of deep learning models with demos.

List of Solutions to ML Problems

http://ndres.me/kaggle-past-solutions/
A sortable and searchable compilation of solutions to past Kaggle competitions.

If you are facing a data science problem, there is a good chance that you can find inspiration here.

Frameworks

Core ML

Libraries/APIs

TensorFlow, Scikit-learn, XGBoost, Keras, Dlib, Node.js

Languages

Python 3, Python, JavaScript 6

Paradigms

Agile

Platforms

Android, iOS, Linux, Apache Kafka

Other

Computer Vision, ML Kit, Computer Vision Algorithms

Tools

Git, Impala

Storage

Aerospike

2013 - 2017

Master's Degree in Computer Engineering

Ecole des Ponts et Chaussées - Paris

AUGUST 2017 - PRESENT

Structuring Machine Learning Projects

Coursera

AUGUST 2017 - PRESENT

Neural Networks and Deep Learning

Coursera

Collaboration That Works

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