Your Team, Your Way

Unity game Development
Unreal Game Development
Coco Game Development

Our latest Publications

Sylvain LIEGE-AWS Certified AI Practitioner certificate
May 5, 2025

Sylvain LIEGE has been certified AWS Certified AI Practitioner.

We are please to share that Sylvain LIEGE has been certified by AWS as AWS Certified AI Practitioner.
AI Training
April 29, 2025

#10 AI Training going wrong

This paper explores why the model might fail in practice: underfitting (too simplistic), overfitting (too rigid), and the underlying issues of bias and variance. Through examples, we show how underfitting leads to random guesses , while overfitting causes oversensitivity. We introduce bias (consistent errors) and variance (prediction variability).
AI Training & Back Propagation
March 25, 2025

#9 AI Training & Back Propagation

AI Training & Back Propagation – In order to use a Digital Neural Network, we need to train it. In this paper we present how we can “train” one using supervised training and backpropagation. By comparing the model’s output with the value that we know to be correct, we can tune the parameters and make it solve the problem at hand.
Unity game Development
Unreal Game Development
Coco Game Development

Our latest Publications

Sylvain LIEGE-AWS Certified AI Practitioner certificate
May 5, 2025

Sylvain LIEGE has been certified AWS Certified AI Practitioner.

We are please to share that Sylvain LIEGE has been certified by AWS as AWS Certified AI Practitioner.
AI Training
April 29, 2025

#10 AI Training going wrong

This paper explores why the model might fail in practice: underfitting (too simplistic), overfitting (too rigid), and the underlying issues of bias and variance. Through examples, we show how underfitting leads to random guesses , while overfitting causes oversensitivity. We introduce bias (consistent errors) and variance (prediction variability).
AI Training & Back Propagation
March 25, 2025

#9 AI Training & Back Propagation

AI Training & Back Propagation – In order to use a Digital Neural Network, we need to train it. In this paper we present how we can “train” one using supervised training and backpropagation. By comparing the model’s output with the value that we know to be correct, we can tune the parameters and make it solve the problem at hand.