nlintz/TensorFlow-Tutorials
TensorFlow Tutorials
هذا المستودع عبارة عن مجموعة من البرامج التعليمية الموجهة لبناء وتدريب نماذج التعلم الآلي باستخدام إطار عمل TensorFlow. يوفر جولات عملية وأمثلة لتنفيذ مجموعة متنوعة من معماريات النماذج لحل مشاكل التنبؤ بالبيانات وتحليلها.
تغطي الأدلة بناء الشبكات العصبية التغذوية، والتلافيفية، والمتكررة لتحليل أنماط البيانات المعقدة. تتضمن برامج تعليمية محددة للتعلم غير الخاضع للإشراف، مثل أجهزة التشفير التلقائي لإزالة الضوضاء وتضمينات word-to-vec، بالإضافة إلى أمثلة لتدريب الشبكات التنافسية التوليدية لتوليف عينات بيانات جديدة.
يتناول المحتوى أيضاً إدارة النماذج، بما في ذلك تعليمات لحفظ واستعادة أوزان الشبكة للحفاظ على تقدم التدريب. بالإضافة إلى ذلك، يغطي تصور مقاييس التدريب والرسوم البيانية الحسابية لمراقبة الأداء.
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.
