Responsible Deep Learning Deployment Program by aivancity

This training course is eligible

www.moncompteformation.gouv.fr

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

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.