lisa-lab/DeepLearningTutorials
DeepLearningTutorials
Ce projet est une ressource éducative et un parcours d'apprentissage pour construire et entraîner des architectures de réseaux de neurones. Il fournit une collection structurée de guides pédagogiques, de notes et d'exercices conçus pour aider les utilisateurs à maîtriser les fondamentaux du développement et du prototypage de modèles d'apprentissage profond.
La ressource se concentre sur la traduction de la théorie conceptuelle de l'apprentissage profond en code exécutable en utilisant une bibliothèque de mathématiques symboliques. Elle inclut des guides et tutoriels spécifiques pour exécuter des calculs de réseaux de neurones sur du matériel graphique afin de réduire le temps d'entraînement des modèles.
Le contenu couvre l'implémentation d'algorithmes d'apprentissage profond, l'utilisation de flux de données basés sur des tenseurs et la construction de modèles utilisant des architectures de couches modulaires. Il associe des concepts théoriques à des implémentations de référence pratiques pour démontrer le processus de construction de modèles entraînables pour des tâches prédictives.
Features
- Learning Paths - Provides a structured learning path consisting of notes and exercises for mastering deep learning fundamentals.
- Deep Learning Education - Provides a comprehensive educational resource for learning neural network theory and practice.
- Deep Learning Prototyping Kits - Provides a library of reference implementations for prototyping and testing early neural network models.
- Neural Network Implementations - Teaches the low-level implementation of neural network architectures and training pipelines.
- Tensor Data Flows - Teaches how to process multidimensional arrays through mathematical transformations to train models.
- Tensor Data Pipelines - Demonstrates the use of tensor data pipelines to stream multidimensional arrays for weight updates.
- Deep Learning Architectures - Provides reference implementations of deep learning architectures for educational purposes.
- Conceptual Code Mappings - Pairs theoretical deep learning concepts with executable code to demonstrate the translation from math to software.
- PyTorch Deep Learning Examples - Offers executable code examples and instructional guides for implementing deep learning models.
- Deep Learning Tutorials - Provides a structured collection of deep learning tutorials, instructional guides, and code examples.
- Neural Network Implementations - Offers practical reference implementations of neural network architectures designed for educational purposes.
- Automatic Differentiation Engines - Explains how to implement automatic differentiation engines for computing gradients in neural networks.
- Tensor Operation Accelerators - Demonstrates how to accelerate tensor operations using GPUs to speed up neural network training.
- GPU Training Accelerators - Provides instructions on using GPU training accelerators to reduce model training time.
- Training Tutorials - Includes step-by-step instructional guides for executing neural network computations on graphics hardware.
- Modular Layer Compositions - Guides the construction of complex neural networks by stacking independent functional layer blocks.
- Modular Architectures - Teaches how to construct neural networks using interchangeable modular blocks.
- Implementation Guides - Guides the translation of conceptual deep learning theory into executable and trainable PyTorch code.
- GPU-Accelerated Computation - Provides instructional material on offloading mathematical operations to graphics hardware for high-performance processing.
- Symbolic Computational Graphs - Explains the use of symbolic computational graphs to automate gradient calculations.
- Deep Learning Frameworks - Official tutorials for deep learning research frameworks.
- Learning and Reference - Deep learning tutorial notes.
