A first edition to learn game creation, electronics and connected objects.
Ages 8 to 13
Explorers
Create, play, understand
A playful journey to discover the digital world and create first creative projects.
Program
Discovering the digital world
Creating animations and mini-games
Introduction to technology and electronics
Final project presentation
Project examples
Mini-games
Interactive animations
Light-up objects
Creative challenges
Personalized projects
Ages 14 to 17
Makers
Imagine, build, experiment
A creative journey to explore programming, artificial intelligence and interactive technologies through concrete projects.
Program
Discovering generative artificial intelligence
Digital creation and creative programming
Introduction to technology and electronics
Final project presentation
Project examples
Creative mini-games
Interactive experiments
AI-assisted projects
Simple electronic objects
Personalized projects
Launch price
149,000 Ar
8 three-hour sessions. Places are limited to ensure good support.
Minimum: 6 participants per group
Maximum: 8 participants per group
Snack and water: provided by the laboratory
Computer: children may bring their own computer, but it is not required
Machine Learning Workshop
Machine Learning: understand neural networks by building them
60,000 Ar per sessionTwo three-hour sessionsSaturdays only
A practical workshop for students, self-taught learners, developers and artificial intelligence enthusiasts who want to understand the real foundations of neural networks and Deep Learning.
About this workshop
This workshop is not a TensorFlow or PyTorch course.
The goal is to understand the fundamental principles behind modern Machine Learning libraries.
Participants will progressively build the essential mechanisms of a neural network to understand how machine learning actually works.
Once these foundations are mastered, using libraries such as TensorFlow or PyTorch becomes much more natural.
What this workshop is
✅ An introduction to the foundations of Deep Learning
✅ A progressive understanding of neural networks
✅ An exploration of the main equations used in Machine Learning
✅ An approach focused on understanding rather than tool usage
✅ A step-by-step construction of the essential learning mechanisms
What this workshop is not
❌ A TensorFlow course
❌ A PyTorch course
❌ A simple use of existing libraries
❌ A series of tutorials to reproduce
Workshop specificity
The concepts studied are independent of the programming language used.
Participants may implement the exercises in the language of their choice, provided they know it well enough.
The examples and explanations remain applicable regardless of the language used.
Python
C#
Java
JavaScript / TypeScript
C++
Go
Rust
Any other language mastered by the participant
Session 1
Foundations of neural networks
Objective
Understand the fundamental principles of Machine Learning and progressively build a first neural network without using a specialized library.
Content
Introduction to Machine Learning
What is Machine Learning?
Types of problems and learning
Why do neural networks exist?
The artificial neuron
Biological inspiration
Inputs, weights and bias
Activation function
Producing a prediction
The perceptron
How it works
Limits of the simple perceptron
Introduction to multilayer networks
Understanding learning
Cost function
Concept of error
Why does a model learn?
Gradient descent
Intuition behind optimization
Slope of a function
Searching for a minimum
Backpropagation
General principle
Adjusting weights
Propagating error
Practice
Progressive construction of a simple neural network
Implementation without a Machine Learning library
Detailed analysis of every calculation step
Expected outcome
Explain how an artificial neuron works
Understand the main learning equations
Explain gradient descent and backpropagation
Build a simple neural network in their own programming language
Session 2
Foundations of convolutional neural networks (CNN)
Objective
Understand why convolutional networks revolutionized image processing and progressively build a first CNN.
Content
Why CNNs?
Limits of classic neural networks
Challenges related to images
The emergence of convolutional networks
Understanding a digital image
Pixels
Color channels
Matrix representation
Convolutions
Fundamental principle
Filters and kernels
Pattern detection
Feature extraction
Feature maps
Feature Maps
Progressive construction of representations
Pooling
Dimensionality reduction
Robustness to variations
CNN architecture
Layer stacking
Data flow through the network
Final classification
Practice
Building a simple CNN
Detailed analysis of calculations
Application to an image classification problem
Expected outcome
Explain how a convolutional network works
Understand the role of convolutions and pooling
Explain how a CNN extracts features from an image
Implement the fundamental principles of a CNN in their own programming language
Prerequisites
Know at least one programming language
Be comfortable with algebra and basic mathematical functions
Understand derivatives and the slope of a curve
Be willing to work with simple mathematical formulas
Bring a personal laptop. Laboratory computers are available in limited numbers
Registration validation
This workshop requires some programming and mathematics foundations.
A short placement test will be conducted before registration is confirmed to ensure that each participant can follow the training in good conditions.
Group size
Workshop held with at least 4 participants
Places limited to 6 participants
This intentionally small format allows personalized support and direct discussion with every participant.
Interested in registering?
Contact us to reserve a place or ask about upcoming dates.
Le Laboratoire Créatif