Yacine Bouaouni

Yacine Bouaouni

Machine Learning Engineer

Valeo Comfort and Driving Assistance

Biography

Welcome to my blog! I’m Yacine, a dedicated machine learning engineer currently working with Valeo Applied Machine Learning Team. With a strong educational background from ENS Paris-Saclay and a Master’s degree in Mathematics, Vision, and Learning (MVA), I have a deep understanding of the intricacies of this exciting field.

During my masters, I had the opportunity to intern at GoPro Paris, where I delved into the fascinating world of “Deep Learning Based Image Deblurring.” This experience further fueled my passion for leveraging cutting-edge technologies to solve real-world problems. Prior to that, I successfully defended my engineering thesis on “Hybrid Deep Learning Based Speech Source Separation in the Time Frequency Domain” at Ecole Nationale Polytechnique.

Passion drives me in exploring the vast applications and theories of machine learning, including computer vision, natural language processing, and speech processing. I remain at the forefront of advancements in these fields, always keeping a close eye on state-of-the-art machine learning methods.

In this blog, I aim to share my knowledge and experiences with you. Expect to find a diverse range of captivating topics, including project showcases, insightful notes, and thought-provoking articles. My goal is to provide valuable resources that will help both beginners embarking on their machine learning journey and seasoned practitioners seeking to stay updated with the latest advancements.

I invite you to join me on this exciting and ever-evolving journey through the world of machine learning. Together, we will explore, learn, and unlock the true potential of this remarkable field.{style=“text-align: justify;”}

Interests
  • Machine Learning
  • Deep Learning
  • Generative Models
  • Computer Vision
  • Natural Language Processing
  • Speech Processing
  • Multi-Modal Learning
  • MLOps
Education
  • M.Sc Mathematics, Vision, Learning (MVA), 2021-2022

    ENS Paris-Saclay (Paris)

  • Engineering Degree in Electronics, 2018-2021

    National Polytechnic School (Algiers)

  • Preparatory Classes in Science and Technology, 2016-2018

    National Polytechnic School (Algiers)

Experience

 
 
 
 
 
Valeo
Machine Learning Engineer
Valeo
October 2022 – Present Paris

Responsibilities:

  • Deep Learning based Visual Inspection
  • Predictive Maintenance
  • MLOps and Model Monitoring
  • Cloud Pipeline Development
 
 
 
 
 
GoPro
Machine Learning & Image Processing Intern
GoPro
April 2022 – September 2022 Paris

Responsibilities:

  • Deep Learning based Image Deblurring
  • Research on Blur Generation Techniques
  • Development of an end-to-end image processing pipeline
 
 
 
 
 
Polytechnic School of Algiers
Deep Learning and Image Processing Research Intern
Polytechnic School of Algiers
February 2021 – July 2021 Algiers

Responsibilities:

  • Research on hybrid deep learning methods for audio source separation in the time frequency domain
  • Research on deep unfolding applied to non-negative matrix factorization
  • Stacking neural networks and non-negative matrix factorization for source separation
 
 
 
 
 
Gustave Eiffel University
Machine Learning Research Intern
Gustave Eiffel University
June 2020 – February 2021 Paris

Responsibilities:

  • Research on driving patterns recognition and time series segmentation
  • Development of an end-to-end unsupervised framework for driving patterns recognition
  • Proposed a method to interpret driving patterns causality using graph theory metrics

Accomplish­ments

Generative AI, Transformers, Diffusion Models, Large Language Models, Generative AI Studio
See certificate
Coursera
Sequences, Time Series and Prediction
See certificate
google
Introduction to Git and Github
See certificate
Coursera
Convolutional Neural Networks
See certificate
mongo
M001: MongoDB Basics
See certificate
coursera
Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization
See certificate
coursera
Neural Networks and Deep Learning
See certificate

Projects

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Auxiliary Classifier GAN for Covid & healthy X-rays image generation
One of the biggest challenges in the medical field is the limitations of the data quantity, especially for training classifiers based on neural networks. The objective of this project is to develop a GAN (AC-GAN) to generate both Covid and healthy X-ray images. The GAN is used to generate a dataset that will be used to train a classifier.
Auxiliary Classifier GAN for Covid & healthy X-rays image generation