Your Team, Your Way

Unity game Development
Unreal Game Development
Coco Game Development

Our latest Publications

AI: Fixing the Training gone Wrong
May 20, 2025

#11 AI: Fixing the Training gone Wrong

Building on Paper #10’s AI training pitfalls—underfitting (too lazy), overfitting (too rigid), high bias (skewed guesses), and high variance (wild swings)—this paper offers practical fixes for our smell detector. We explore three levers: boosting network capacity, extending training with more epochs, and enriching data for smarter learning.
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).
Unity game Development
Unreal Game Development
Coco Game Development

Our latest Publications

AI: Fixing the Training gone Wrong
May 20, 2025

#11 AI: Fixing the Training gone Wrong

Building on Paper #10’s AI training pitfalls—underfitting (too lazy), overfitting (too rigid), high bias (skewed guesses), and high variance (wild swings)—this paper offers practical fixes for our smell detector. We explore three levers: boosting network capacity, extending training with more epochs, and enriching data for smarter learning.
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).