Industrialization
Quickly industrialize your AI algorithm into a production environment
Latest projects
This project consist in a creation of a several filters algorithm on top of a kinect pose detection to smooth and improve real time light saber position inside a unity project.
What has been done:
1. Extraction of kinect pose detection output
2. Parametrize and fine tuning of Kalman and oneeuro real time filters
3. Conception of a python library to deploy easily over unity
4. Real time post processing using zeroMQ
GinoLegalTech is a company which business consist to automate and manage legal contract.
Every contract build inside Gino workflow has a skeleton known as Logical Question Answering (LQA).
This LQA is composed with several question-answers items such as:
Question: What is the partie ?
Answer: "GinoLegalTech"
The main task was to obtain this LQA from any external contract using nlp skills.
Such task has been done using image processing, OCR, word embedding, classification, NER and depency parser.
Extraction are various and can be added by anyone.
Link between such extraction are supported.
Workflow from user experience are trained to improve all the models.
Other task such as clause detection which need proofreading and differences between two texts has also be done.
Making annotation application tools in Qt to optimize it.
Creating some tools for MLops and Devops optimization (Docker, Automatic models updates with their metrics, etc ...)
Collaborating with cross-functional teams and customers to identify product improvements.
Managing and implementing the project in a SaaS solution with a developers team based in Shenzhen
This project consist in a creation of a OCR / translation service. It can detect and replace the text content of a picture.
What has been done:
1. OCR via Tesseract
2. Algorithm to detect text colors
3. Translation based on transformer
5. Containerize this application with Docker in order to deploy it easily
This project consist in a creation of a machine learning algorithm which detect entities from a picture (person, object, car, etc ...)
What has been done:
1. Refactorisation of a YoloV7 algorithm
2. Creation of an opensource library Imlab
3. Creation of ML ops and Dev ops pipeline
5. Containerize this application with Docker in order to deploy it easily
This project consist in a creation of a machine learning algorithm based on perceptron which interpollate points in order to build a curve.
What has been done:
1. Creation of a perceptron algorithm in Cython
2. Optimisation of this algorithm
3. Accept input from CSV and display it on Vispy / D3
This project consist in a creation of a machine learning algorithm which detect faces and replace it by a smiling emoji if the face is smiling or a non smiling emoji.
What has been done:
1. Creation of a smiling dataset
2. Use of a computer vision algorithm to detect a face
3. Training of a vision machine learning algorithm to detect if a face is smiling or not
4. Conception and optimization of the relatives pipeline to run it on live video
5. Containerize this application with Docker in order to deploy it easily
This project has for objective to optimize prediction of stock exchange. The main idea consisted in extrapolating stock share option data in real time. It is separated in several distinct sub project.
1. Obtaining a data set: to obtain such data, a web scrapping python program has to be developed. It uses selenium and beautifulsoup to parse html data. It must also be in real time. As soon as the web scrapping application took the stock share option information, it is stocked on a web server in a redis database.
2. Use deep and machine learning algorithm to extrapolate those data: LSTM, percrepton, random forest and a real time peak detection has been used to take the best decision compared to the risk.
3. Optimization - speed calculation: every algorithm has been optimized to process the result with less than 1 second thanks to C++ and fortran algorithm conversion.
4. Visual display: tools such as OpenCL and Vispy has been used to display such result with a latency of less than 30 ms.
Total is a major company in oil and gas industry, present all over the world.
The project consisted in training a neural network to model seismic attributes in order to find underground oil. This task required both geophysics and machine/deep learning skills.
Within the framework of locating oil methodologies, the main issue is the large process time of seismic data. In order to image the underground, one must realize seismic acquisition through sound wave propagation. The records of seismic waves data are done depending on time. One seeks to convert time into depth: this process is the inversion. After the inversion, data are processed to obtain specific seismic attributes (for example, rock porosity) in order to find potential oil.
Based on well data and seismic profiles, deep learning is used to speed up all the process. Indeed, well data are used to learn the association between seismic attributes and well's seismic profile, then a generalization is done on the entire seismic profile.
VMPS - ProfenPoche is a company which sell online course subscription for math.
This project has allowed the creation of a chat bot which can solve step by step math exercise and send math course about the mathematical subject taken by a camera.
What has be done:
• Creation of an Artificial Intelligence for a scholar application: for a given photo, with Tesseract OCR, send to students the course and exercises content associated with that image. Detects the presence of equations and converts them into LaTeX format. Can also solve some mathematical problems
• Development of a neural network based on the seq2seq model to improve the artificial intelligence result
• Use of growth hacking method for a mobile application; impact evaluation of several marketing approaches
• Contribution in the Business Plan development and the company financial analysis
• Speed and precision improvement of the artificial Intelligence created previously.
• Creation of another artificial Intelligence to solve any mathematical problem from middle to high school “step by step” by creating a LaTeX sheet
