What are the main features of codebasics/deep-learning-keras-tf-tutorial?

The main features of codebasics/deep-learning-keras-tf-tutorial are: Deep Learning Training Toolsets, Gradient-Based Parameter Updates, Loss Function Implementations, Modular Layer Compositions, Keras Model Implementations, Stochastic Gradient Descent, Deep Learning Tutorials, Machine Learning Tutorials.

Deep Learning Keras Tf Tutorial - apprendre l…

Ce projet est un programme éducatif structuré conçu pour enseigner les fondamentaux de la construction et de l'entraînement de modèles d'apprentissage profond. Il fournit un guide complet pour implémenter des réseaux de neurones en utilisant des frameworks de machine learning de haut niveau et le langage de programmation Python, en se concentrant sur des exercices pratiques et concrets pour les débutants.

Le tutoriel se distingue en couvrant le cycle de vie complet du développement de modèles, de la construction initiale à l'optimisation prête pour la production. Il inclut des modules spécifiques sur l'affinement des performances du modèle via la quantification des poids et la résolution du biais des données en atténuant les déséquilibres de classe. Le programme souligne également l'importance de la préparation des données, offrant des techniques pour l'augmentation d'images et la création de plongements de mots (word embeddings) pour améliorer la généralisation du modèle.

Au-delà de l'entraînement de base, le dépôt explore des tâches avancées de traitement du langage naturel et de vision par ordinateur. Il démontre comment construire des modèles de transformateurs, utiliser des réseaux de neurones récurrents pour la classification de texte et optimiser les pipelines d'entrée de données pour garantir un traitement efficace. Le matériel couvre également des pratiques de surveillance essentielles, telles que la visualisation des métriques d'entraînement et des fonctions de perte pour évaluer la précision du modèle tout au long du processus d'apprentissage.

Features

Deep Learning Training Toolsets - Provides a comprehensive toolkit for building and training deep learning models from scratch.
Gradient-Based Parameter Updates - Updates model parameters iteratively using gradient-based backpropagation to minimize loss.
Loss Function Implementations - Calculates training loss to guide the optimization process during neural network training.
Modular Layer Compositions - Constructs neural networks by stacking modular functional layers to transform input data.
Keras Model Implementations - Offers a structured educational guide for implementing deep learning models using the Keras API.
Stochastic Gradient Descent - Optimizes model weights using stochastic gradient descent to navigate the loss landscape.
Deep Learning Tutorials - Serves as an educational resource for building and training neural networks using high-level deep learning frameworks.
Machine Learning Tutorials - Provides a comprehensive curriculum of tutorials for constructing and training predictive models through hands-on exercises.
Class Imbalance Handling - Mitigates data bias by adjusting training weights to handle class imbalances in datasets.
Data Input Pipelines - Optimizes data input pipelines to ensure efficient processing and prevent idle hardware time.
Data Prefetching Pipelines - Implements background data prefetching to keep hardware accelerators saturated during model training.
Model Quantization Tools - Reduces model precision to decrease memory usage and accelerate inference on resource-constrained hardware.
Machine Learning Optimization - Provides techniques for optimizing neural network performance, including weight quantization and data balancing strategies.
Computer Vision Modelings - Provides modular components for computer vision tasks including data augmentation to reduce overfitting.
Natural Language Processing - Covers natural language processing tasks including transformer model construction and word embedding generation.
Word Embeddings - Learns dense vector representations of words to capture semantic relationships for NLP tasks.
Weight Quantization - Compresses model weights through quantization to reduce memory footprint and accelerate inference.
Self-Attention Mechanisms - Utilizes self-attention mechanisms to weigh sequence importance for improved prediction accuracy.
Text Classifiers - Categorizes text sequences using recurrent neural networks to learn patterns from training data.
Image Augmentations - Improves model generalization by applying random image transformations like rotation and flipping.
Transformer Language Models - Constructs transformer-based language models for natural language processing tasks.
Computational Graphs - Defines and executes neural network models using compiled computational graphs for high-performance processing.
Training Metric Monitors - Tracks training progress and loss metrics to evaluate model accuracy during the learning process.

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bert4keras is a lightweight reimplementation of the BERT transformer architecture for the Keras deep learning framework. It serves as a natural language processing toolkit and transformer model library used for text classification, sequence labeling, and semantic embedding extraction. The framework includes a sequence-to-sequence model system for question answering and text generation, as well as a model inference server to deploy trained transformers as web APIs for real-time predictions. Capabilities cover a broad range of natural language understanding tasks, including reading comprehensi

codebasicsdeep-learning-keras-tf-tutorial