Démarrer les réseaux de neurones Architectures avancées des modèles

Aperçu

Neural networks power the intelligent systems transforming industries today—from autonomous vehicles to personalized recommendations. This Short Course was created to help data analysts accomplish the critical transition from traditional machine learning to deep learning architectures.

By completing this course, you'll be able to design, implement, and optimize neural networks that meet real-world performance standards while preventing overfitting through systematic evaluation. By the end of this course, you will be able to:

Build feed-forward neural networks using Keras/PyTorch with documented architecture decisions Evaluate model performance through learning-curve analysis and validation metrics Implement regularization techniques to achieve specified generalization targets This course is unique because it combines theoretical foundations with hands-on implementation, emphasizing both performance achievement and systematic documentation practices essential for production environments.

To be successful in this project, you should have a background in Python programming, basic machine learning concepts, and familiarity with data preprocessing techniques.

Programme

• Module 1 : Mise en œuvre du réseau feed-forward - Fondations
Construire un réseau de neurones feed-forward en utilisant Keras/PyTorch, atteindre une perte de validation spécifiée, et documenter les choix d'architecture.
• Module 2 : Évaluation du surapprentissage et régularisation - Application principale
Évaluer le surapprentissage via l'analyse des courbes d'apprentissage et implémenter la régularisation (dropout/L2) pour atteindre les objectifs de généralisation.

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Enseigné par

Hurix Digital

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Matières

Artificial Intelligence