nlintz/TensorFlow-Tutorials
TensorFlow Tutorials
Ce dépôt est une collection de tutoriels guidés pour construire et entraîner des modèles de machine learning en utilisant le framework TensorFlow. Il fournit des procédures pratiques et des exemples pour implémenter une variété d'architectures de modèles afin de résoudre des problèmes de prédiction et d'analyse de données.
Les guides couvrent la construction de réseaux de neurones feedforward, convolutifs et récurrents pour analyser des modèles de données complexes. Il inclut des tutoriels spécifiques pour l'apprentissage non supervisé, tels que les auto-encodeurs de débruitage et les embeddings word-to-vec, ainsi que des exemples pour entraîner des réseaux antagonistes génératifs (GAN) afin de synthétiser de nouveaux échantillons de données.
Le contenu aborde également la gestion des modèles, incluant des instructions pour enregistrer et restaurer les poids des réseaux afin de persister la progression de l'entraînement. De plus, il couvre la visualisation des métriques d'entraînement et des graphes de calcul pour surveiller les performances.
Features
- Machine Learning Implementations - Provides a collection of guided code implementations for building and training various machine learning models.
- Machine Learning Tutorials - Offers a collection of guided tutorials and examples for building and training machine learning models.
- Linear and Logistic Regression - Includes tutorials for implementing linear and logistic regression to model variable relationships.
- Neural Network Construction - Guides the design and construction of deep and convolutional neural network architectures.
- Neural Network Model Implementations - Provides implementations for a variety of neural network architectures including feedforward, convolutional, and recurrent networks.
- Unsupervised Learning - Implements unsupervised learning techniques including denoising autoencoders and word embeddings.
- Generative Adversarial Network Training - Provides practical examples for training generative adversarial networks to synthesize new data.
- Keras Abstractions - Illustrates the construction of models using Keras layer abstractions for simplified architecture definition.
- Model Performance Visualizations - Includes tutorials for visualizing model performance metrics and computational graphs.
- Adversarial Training Procedures - Demonstrates the training of adversarial networks to synthesize new data samples.
- Model Weight Management - Covers the management, storage, and loading of model weights to persist training progress.
- Weight Persistence - Provides instructions for saving and restoring model weights and training states to disk.
- Generative Adversarial Networks - Provides tutorial examples for constructing and training generative adversarial network architectures.
- Model State Management - Includes instructions for saving and restoring network weights to persist training progress and reuse models.
- Model Weight Persistence - Explains how to save and restore network weights to reuse trained models across sessions.
- Training Progress Monitors - Provides tools for real-time visualization of training metrics and computational graphs.
- Training Metric Monitors - Covers the tracking of training and validation accuracy to monitor model performance and overfitting.
- Deep Learning Frameworks - Practical guides and code snippets for TensorFlow development.
- Deep Learning Tutorials - Introductory guides for building models with TensorFlow.
- Learning and Reference - Simple TensorFlow tutorials.
- Educational Tutorials - Practical examples for learning TensorFlow.
