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Majella ELOBO EKASSI
Email : ​eloekamaje@gmail.com
Tel: +237 676429658/ 690103569
Skype : eloekamje
Machine learning Engineer
(3 years experience)
EDUCATION/TRAINING
Diploma Domain Year School
Master Degree Machine learning 2014 Faculty of Sciences of
University of Yaoundé 1
Bachelor Degree Computer science 2011 Faculty of Sciences of
University of Yaoundé 1
CERTIFICATIONS 
- Machine Learning Certification Date Jul 2017 – Present License MJF9W8VJFRFL, Url verification:
https://www.coursera.org/account/accomplishments/verify/MJF9W8VJFRFL
- Neural Networks and Deep Learning Certification Date Oct 2017 – Present License JVSGUWGSAP8R, Url
verification: ​https://www.coursera.org/account/accomplishments/verify/JVSGUWGSAP8R
- Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization Certification
Date Nov 2017 – Present License VY3M84H3E348, Url verification:
https://www.coursera.org/account/accomplishments/verify/VY3M84H3E348
FEATURED SKILLS AND ENDORSEMENTS 
● Image processing;
● Develop, train, test and apply conventional machine learning algorithm;
● Develop, train, test and apply deep learning algorithm (Convolutional Neural networks,
Recurrent neural networks) ;
● Improve and optimize conventional machine learning algorithm and deep neural networks;
● Python, C++, Java programming;
● training and deploying model on distributed architectures
WORK HISTORY 
Mr. ELOBO EKASSI Majella graduated from the University of Yaoundé 1 with a master's degree in computer
science, basic machine learning option since 2011. Mr. ELOBO EKASSI Majella has worked in offshore projects for
a German partner company named Pama technologies (​www.pama-tech.com​) in which he has developed an
expertise focused on image processing and deep learning.
Mr. ELOBO EKASSI Majella has qualities of autonomy, adaptation, innovation and team leadership. Which makes
him a highly valued resource.
FROM AUGUST 2016 TO TODAY - GLOBAL DYNAMICS TECHNOLOGIES
Mr. ELOBO EKASSI Majella has held the position of engineer at Global Dynamics Technologies (GDT) from August
2016 until today. As part of his duties, Mr. ELOBO EKASSI Majella has mainly played the role of engineer in image
processing and machine learning in several projects of the company. Mr. ELOBO EKASSI Majella participated in
the following projects:
Project 1 : SELECTR
Position: Machine learning engineer
Project description : ​Every day, thousands of images enter the databases of editorial systems and become a
confusing and often impenetrable bazaar. These images are usually accompanied by an inconsistent, incomplete
and sometimes incorrect bibliography, as well as linguistic barriers to textual metadata.
SELECTR is therefore this intelligent search application in a large mass of image data that allows publishers of
news agencies to be able to search efficiently and accurately in their masses of data, in order to remain
competitive in the field. processing time and publication of information. In SELECTR End User application,
publishers select a set of semantic filters based on metadata generated by Deep Learning algorithms to make
effective access to accurate images containing specific people and concepts. The main filters on SELECTR are:
- Logo search which use logo recognition model to find image containing a specific or a set of logos
- person search which use person recognition models trained to find images with a set of personalities
- Gesture search which use a gesture detection model and gaze estimation model in other to retrieve
images with persons acting with a specific set of gestures (Hands outstretched, Celebrating, looking at camera,
etc.)
- Scene search which use fine grained scene recognition model to retrieve images containing a specific
scene
- person counter in other to find images with specific number of persons
- Facial expression filter for finding images with person have a particular facial expression recognition
(Happy, sad, angry, neutal)
Many filters can be selected on SELECTR and result images metadata are combination of metadata generated by
each filter.
Intervention : ​As a machine learning engineer, Mr. ELOBO EKASSI Majella performed the following tasks:
● Gather knowledge (brainstorming, research article)
● Gather, clean data to build datasets
● Train, optimize models
● Test and evaluate trained models
● Deploy models in production workflow
Technologies​ : caffe framework, python, CNN, py-faster-RCNN (object detection framework), keras framework,
SCRUM (JIRA), Git
Project 2 : SELECTR for Deutsche Presse-Agentur (DPA)
Position: Machine learning engineer
Project description : ​This project is based on project 1 with only person recognition model. DPA (Deutsche
Presse-Agentur) use it to search images containing specific players or coaches of Bundesliga first and second
leagues, or german political celebrities.
Intervention : ​As a machine learning engineer, Mr. ELOBO EKASSI Majella performed the following tasks:
● Gather knowledge (brainstorming, research article)
● Gather, clean data to build datasets
● Extract features from Google neural network architecture for face recognition (Facenet)
● Train, optimize SVM models
● Test and evaluate trained models
● Deploy models in production workflow
●
Technologies​: Tensorflow, FaceNet (Google neural network architecture for face recognition), Support Vector
Machine (SVM), python3, OpenCV, SCRUM (JIRA), Git
Project 3 : Sponsor logo analysis in 2019 IIHF ICE HOCKEY WORLD CHAMPIONSHIP videos
matches
Position: Machine learning engineer
Project description : ​This project consists in developing a service which deliver csv files containing metadata
which describe the statistics of appearance of all sponsor logos in videos of all matches during 2019 IIHF Ice
Hockey World Cup. The statistics are the number of time a logo appear, the mean area ratio occupied by the logo
in arenas. The logos are located in ringboard of the arena, in player clothes sleeves and helmet, and in bench.
Some of the logos are text so an OCR based detection model + a Convolutional Recurrent Neural Network
(CRNN) are used to recognize them. The others logos are detected with object detection model trained with
YOLOv3 deep convolutional network architecture with a bootstrapping approved which used our own synthetic
object detection dataset generator. To proceed models in a video efficiently, the video is divided in keyframes by
applying shot detection.
Intervention : ​As a machine learning engineer, Mr. ELOBO EKASSI Majella performed the following tasks:
● Implement synthetic object detection dataset generator
● Implement Shot cut detection algorithm and evaluate them in videos matches
● Implement the bootstrapping framework
● Deploy OCR based models and Text recognition model
● Implement algorithm for shot detection in videos
● Train, optimize YOLOv3 models to recognize non text logos with bootstrapping framework.
● Deploy YOLOv3 models in production workflow.
Technologies​ : Mxnet framework (YOLOv3), caffe framework (TextBoxes++), Pytorch (CRNN), Python, Pillow
Opencv, SCRUM (JIRA), Git
Project 4 : Polizei security project
Position: Machine learning engineer
Project description : ​In the context of public demonstrations, the police would like to have tools that allow
them to have to monitor the protesters in order to prevent or identify the troublemakers. It is with this in mind
that the Police Security project provides the possibility to estimate the number of protesters, to have a detailed
description of the protesters in terms of race, accessories, clothing, gender and age. To achieve these goals, this
project use a object detection model able to detect roughly 1200 different objects and their color attributes. The
object detection model is based on py-faster RCNN algorithm with Resnet 101 backbone architecture.
Intervention : ​As a machine learning engineer, Mr. ELOBO EKASSI Majella performed the following tasks:
● Train, optimize and evaluate py-faster RCNN based model
● Implement post-processing module
● Deploy py-faster RCNN based model in production workflow
Technologies​ : Python, Caffe framework, faster-RCNN algorithm, SCRUM (JIRA), Git
Project 5 : Fassoo
Position: Machine learning engineer
Project description : ​This project is based on project 1. All the semantic filters of project 1 are enable but they
are applied to movies videos. The movies are divided in key frames with shot cut algorithm (Hard cut + Transition
cut). the idea behind is to fine semantic information across vidéos.
Intervention : ​As a machine learning engineer, Mr. ELOBO EKASSI Majella performed the following tasks:
● Implement Shot cut detection algorithm and evaluate them in movies videos
● Deploy Project 1 models in production workflow for movies videos
Technologies​: caffe framework, python, CNN, py-faster-RCNN (object detection framework), keras framework,
SCRUM (JIRA), Git
TECHNOLOGIES
Programming languages Python, Java, C++
Image processing Tool OpenCV, Pillow
Data processing Pandas, numpy
Machine learning Framework Scikit-learn (Regression, classification, Clustering)
Deep learning frameworks Caffe, Pytorch, Keras, Tensorflow, Mxnet
Versionning Git
Agile method SCRUM ​ (JIRA)
Knowledge Management System Confluence
PERSONAL QUALITIES 
❖ Good proposal strength
❖ Good organization and rigor in the work
❖ Good technical analysis and synthesis skills
❖ Good risk assessment
❖ Adaptability and Autonomy in the work
❖ Strong ability to work under pressure
❖ Listening and opening to critics, Team spirit
❖ Spirit of Leadership and Management
LANGUAGES AND VARIOUS 
French: fluently speaking, listening, writing
English: professional level

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CV machine learning freelancer

  • 1. Majella ELOBO EKASSI Email : ​eloekamaje@gmail.com Tel: +237 676429658/ 690103569 Skype : eloekamje Machine learning Engineer (3 years experience) EDUCATION/TRAINING Diploma Domain Year School Master Degree Machine learning 2014 Faculty of Sciences of University of Yaoundé 1 Bachelor Degree Computer science 2011 Faculty of Sciences of University of Yaoundé 1 CERTIFICATIONS  - Machine Learning Certification Date Jul 2017 – Present License MJF9W8VJFRFL, Url verification: https://www.coursera.org/account/accomplishments/verify/MJF9W8VJFRFL - Neural Networks and Deep Learning Certification Date Oct 2017 – Present License JVSGUWGSAP8R, Url verification: ​https://www.coursera.org/account/accomplishments/verify/JVSGUWGSAP8R - Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization Certification Date Nov 2017 – Present License VY3M84H3E348, Url verification: https://www.coursera.org/account/accomplishments/verify/VY3M84H3E348 FEATURED SKILLS AND ENDORSEMENTS  ● Image processing; ● Develop, train, test and apply conventional machine learning algorithm; ● Develop, train, test and apply deep learning algorithm (Convolutional Neural networks, Recurrent neural networks) ; ● Improve and optimize conventional machine learning algorithm and deep neural networks; ● Python, C++, Java programming; ● training and deploying model on distributed architectures WORK HISTORY  Mr. ELOBO EKASSI Majella graduated from the University of Yaoundé 1 with a master's degree in computer science, basic machine learning option since 2011. Mr. ELOBO EKASSI Majella has worked in offshore projects for a German partner company named Pama technologies (​www.pama-tech.com​) in which he has developed an expertise focused on image processing and deep learning.
  • 2. Mr. ELOBO EKASSI Majella has qualities of autonomy, adaptation, innovation and team leadership. Which makes him a highly valued resource. FROM AUGUST 2016 TO TODAY - GLOBAL DYNAMICS TECHNOLOGIES Mr. ELOBO EKASSI Majella has held the position of engineer at Global Dynamics Technologies (GDT) from August 2016 until today. As part of his duties, Mr. ELOBO EKASSI Majella has mainly played the role of engineer in image processing and machine learning in several projects of the company. Mr. ELOBO EKASSI Majella participated in the following projects: Project 1 : SELECTR Position: Machine learning engineer Project description : ​Every day, thousands of images enter the databases of editorial systems and become a confusing and often impenetrable bazaar. These images are usually accompanied by an inconsistent, incomplete and sometimes incorrect bibliography, as well as linguistic barriers to textual metadata. SELECTR is therefore this intelligent search application in a large mass of image data that allows publishers of news agencies to be able to search efficiently and accurately in their masses of data, in order to remain competitive in the field. processing time and publication of information. In SELECTR End User application, publishers select a set of semantic filters based on metadata generated by Deep Learning algorithms to make effective access to accurate images containing specific people and concepts. The main filters on SELECTR are: - Logo search which use logo recognition model to find image containing a specific or a set of logos - person search which use person recognition models trained to find images with a set of personalities - Gesture search which use a gesture detection model and gaze estimation model in other to retrieve images with persons acting with a specific set of gestures (Hands outstretched, Celebrating, looking at camera, etc.) - Scene search which use fine grained scene recognition model to retrieve images containing a specific scene - person counter in other to find images with specific number of persons - Facial expression filter for finding images with person have a particular facial expression recognition (Happy, sad, angry, neutal) Many filters can be selected on SELECTR and result images metadata are combination of metadata generated by each filter. Intervention : ​As a machine learning engineer, Mr. ELOBO EKASSI Majella performed the following tasks: ● Gather knowledge (brainstorming, research article) ● Gather, clean data to build datasets ● Train, optimize models ● Test and evaluate trained models ● Deploy models in production workflow Technologies​ : caffe framework, python, CNN, py-faster-RCNN (object detection framework), keras framework, SCRUM (JIRA), Git Project 2 : SELECTR for Deutsche Presse-Agentur (DPA) Position: Machine learning engineer Project description : ​This project is based on project 1 with only person recognition model. DPA (Deutsche Presse-Agentur) use it to search images containing specific players or coaches of Bundesliga first and second leagues, or german political celebrities. Intervention : ​As a machine learning engineer, Mr. ELOBO EKASSI Majella performed the following tasks:
  • 3. ● Gather knowledge (brainstorming, research article) ● Gather, clean data to build datasets ● Extract features from Google neural network architecture for face recognition (Facenet) ● Train, optimize SVM models ● Test and evaluate trained models ● Deploy models in production workflow ● Technologies​: Tensorflow, FaceNet (Google neural network architecture for face recognition), Support Vector Machine (SVM), python3, OpenCV, SCRUM (JIRA), Git Project 3 : Sponsor logo analysis in 2019 IIHF ICE HOCKEY WORLD CHAMPIONSHIP videos matches Position: Machine learning engineer Project description : ​This project consists in developing a service which deliver csv files containing metadata which describe the statistics of appearance of all sponsor logos in videos of all matches during 2019 IIHF Ice Hockey World Cup. The statistics are the number of time a logo appear, the mean area ratio occupied by the logo in arenas. The logos are located in ringboard of the arena, in player clothes sleeves and helmet, and in bench. Some of the logos are text so an OCR based detection model + a Convolutional Recurrent Neural Network (CRNN) are used to recognize them. The others logos are detected with object detection model trained with YOLOv3 deep convolutional network architecture with a bootstrapping approved which used our own synthetic object detection dataset generator. To proceed models in a video efficiently, the video is divided in keyframes by applying shot detection. Intervention : ​As a machine learning engineer, Mr. ELOBO EKASSI Majella performed the following tasks: ● Implement synthetic object detection dataset generator ● Implement Shot cut detection algorithm and evaluate them in videos matches ● Implement the bootstrapping framework ● Deploy OCR based models and Text recognition model ● Implement algorithm for shot detection in videos ● Train, optimize YOLOv3 models to recognize non text logos with bootstrapping framework. ● Deploy YOLOv3 models in production workflow. Technologies​ : Mxnet framework (YOLOv3), caffe framework (TextBoxes++), Pytorch (CRNN), Python, Pillow Opencv, SCRUM (JIRA), Git Project 4 : Polizei security project Position: Machine learning engineer Project description : ​In the context of public demonstrations, the police would like to have tools that allow them to have to monitor the protesters in order to prevent or identify the troublemakers. It is with this in mind that the Police Security project provides the possibility to estimate the number of protesters, to have a detailed description of the protesters in terms of race, accessories, clothing, gender and age. To achieve these goals, this project use a object detection model able to detect roughly 1200 different objects and their color attributes. The object detection model is based on py-faster RCNN algorithm with Resnet 101 backbone architecture. Intervention : ​As a machine learning engineer, Mr. ELOBO EKASSI Majella performed the following tasks: ● Train, optimize and evaluate py-faster RCNN based model ● Implement post-processing module ● Deploy py-faster RCNN based model in production workflow
  • 4. Technologies​ : Python, Caffe framework, faster-RCNN algorithm, SCRUM (JIRA), Git Project 5 : Fassoo Position: Machine learning engineer Project description : ​This project is based on project 1. All the semantic filters of project 1 are enable but they are applied to movies videos. The movies are divided in key frames with shot cut algorithm (Hard cut + Transition cut). the idea behind is to fine semantic information across vidéos. Intervention : ​As a machine learning engineer, Mr. ELOBO EKASSI Majella performed the following tasks: ● Implement Shot cut detection algorithm and evaluate them in movies videos ● Deploy Project 1 models in production workflow for movies videos Technologies​: caffe framework, python, CNN, py-faster-RCNN (object detection framework), keras framework, SCRUM (JIRA), Git TECHNOLOGIES Programming languages Python, Java, C++ Image processing Tool OpenCV, Pillow Data processing Pandas, numpy Machine learning Framework Scikit-learn (Regression, classification, Clustering) Deep learning frameworks Caffe, Pytorch, Keras, Tensorflow, Mxnet Versionning Git Agile method SCRUM ​ (JIRA) Knowledge Management System Confluence PERSONAL QUALITIES  ❖ Good proposal strength ❖ Good organization and rigor in the work ❖ Good technical analysis and synthesis skills ❖ Good risk assessment ❖ Adaptability and Autonomy in the work ❖ Strong ability to work under pressure ❖ Listening and opening to critics, Team spirit
  • 5. ❖ Spirit of Leadership and Management LANGUAGES AND VARIOUS  French: fluently speaking, listening, writing English: professional level