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Villejuif Campus
151 Maxime Gorki Boulevard, 94800 Villejuif
Nice Campus
4 Bd Carabacel, 06000 Nice
This training course is eligible
Prerequisites
Prior knowledge of data science and machine learning.
Upcoming school years
June 12, 13, 26, and 27 - July 10: exam date
Format
1,950 Euros excl.
Location
Paris-Villejuif Campus
13
Martial HENNIQUE
Admissions Manager
Certification
This module is the third and final part of the certificate "Neural Networks and Deep Learning" certificate. The goal is to deploy a responsible solution based on deep learning, focusing on interpretability and social and ethical implications.
This certification is valid for 5 years.
You can choose to prepare for the entire qualification gradually over time or have your additional skills recognized through the VAE (validation of acquired experience) process.
Info
Discover the program's assessment and teaching methods
Click here to find out more about the program's evaluation and teaching methods.
Performance indicators 2022 / 2023
Content updated on 19/11/2023
96%
Satisfaction rate
92%
Graduation rate
100%
Graduation rate
Objectives
This course will provide you with:
- Understanding deep neural networks
- Hands-on experience with deploying and hosting an AI model.
- Tools for designing responsible deep learning solutions that address social and ethical challenges.
Contents
Module 1
Interpretability of artificial neural networks
How can we properly understand and explain what happens in an artificial neural network? Modeling in the form of a "black box."
The main concepts covered in this module are: interpretability, action plan and policy of a model, and the Python libraries SHAP and LIME.
Module 2
Deployment of a deep learning model
A concrete example of deploying a deep learning model and a focus on a few specialized frameworks.
The main concepts covered in this module are: deployment, data science workload, and Strealit and FastAPI frameworks.
Module 3
Responsible deployment of a deep learning model
Focus on model interpretability and consideration of the social and ethical implications of deep learning.
The main concepts covered in this module are: deep learning models, interpretability, deployment.
