Abstract: A racing competition held in France asked players to design an autonomous toy car, piloted with deep learning models. Following a line is a well known problem in computer science, and there is no need for a neural network to perform such a task. However, I decided to assemble hardware components (rapsberry, arduino) and design some code in python to make an autonomous radio control car, using only a camera.
The deep learning approach implies to have training examples to quantify the weighs of the architecture. You can either have real case examples that you generate while manually piloting the car. This method can be defined as shaping the model, which is not satisfying because it may produce unexpected biaises. I decided to adopt a simulation technique to generate my training examples. As a result, the car is autonomous without having ever driven on the race. I would like to share with you the general workflow, from scratch, ranging from hardware assembling to racing in production, via training models.
Bio: I am strongly interested in the creation of value out of data, and we are currently experiencing one of the most satisfying mind shifts of the past 10 years. Companies start to realize that data is not a useless expense to build on, but a real opportunity to assess their results, find insights in their process failures, reclaim their expertise and probably evolve to a more sustainable business. I want to help those who believe in such a potential by accelerating their transition toward a data driven company. In order to address these new problematics, I focus on mastering every skill of a complete Data Geek : architecture expertise (data, applications, network), data science mastering (statistical learning, data visualisation, algorithmic theory), customer and business understanding (model prediction consumption, business metrics, customer needs).
I have been working for about two years for OCTO Technology in the best Big Data team in France. I am an expert in the industry sector and I work on several types of mission, ranging from predictive maintenance of production site, to prediction of critical KPIs in video games, via real time monitoring of manufacturing devices. Prior to joining OCTO, I was working as a researcher in data61 (formerly known as NICTA), the best research institute in ICT in Australia on applying Machine Learning to profile GUI users and provide the best amount of information to help them make a decision based on a machine learning prediction