Spandan-Madan/DeepLearningProject
DeepLearningProject
Este proyecto es un pipeline de clasificación multietiqueta diseñado para la predicción de géneros. Implementa un flujo de trabajo de aprendizaje automático que asigna múltiples etiquetas de categoría a un solo elemento procesando datos de entrada tanto textuales como visuales.
El sistema utiliza extracción de características multimodal para transformar imágenes y descripciones de texto en vectores semánticos. Este proceso incluye el uso de redes preentrenadas para la extracción de características visuales y el promedio de palabras semánticas para el análisis de texto, permitiendo que el modelo integre diferentes tipos de datos en una entrada unificada.
El pipeline cubre todo el ciclo de vida del aprendizaje automático, incluyendo la integración de metadatos de conjuntos de datos desde bases de datos externas y la organización de los datos en un pipeline lineal de múltiples etapas. El rendimiento se mide mediante la evaluación de métricas de verdad fundamental utilizando cálculos de precisión y exhaustividad (recall), mientras que las relaciones entre categorías se analizan mediante matrices de coocurrencia por pares.
Features
- Visual-Language Multimodal Integration - Combines visual features from images and semantic vectors from text into a unified input for genre prediction.
- Lexical Feature Extractions - Extracts textual features from descriptions using word embedding averaging and stop-word removal.
- Multimodal Feature Extractors - Implements multimodal feature extraction to combine image and text data into unified vectors for genre classification.
- Ground-Truth Scoring - Evaluates model success by comparing predicted category vectors against original labels using precision and recall.
- Machine Learning Pipelines - Builds a complete machine learning workflow from custom data generation to deep learning model execution.
- Model Evaluation Metrics - Evaluates classification accuracy using precision and recall metrics compared to ground truth labels.
- Model Performance Evaluators - Measures prediction accuracy by comparing predicted labels against ground-truth data using precision and recall.
- Semantic Word Averaging - Converts textual plot descriptions into feature vectors by averaging word embeddings after filtering stop words.
- Pre-trained Neural Feature Extraction - Transforms raw images and text into high-dimensional vectors using pre-trained network layers.
- Multi-Label Classifiers - Implements a machine learning pipeline that assigns multiple category labels to a single item.
- Genre Prediction - Trains neural networks to assign multiple genre labels based on textual and visual features.
- Multi-Label Prediction Analysis - Assigns multiple genre labels to items using a neural network trained on binarized feature vectors.
- Configurable Stage Pipelines - Organizes the machine learning workflow into a sequential multi-stage pipeline from ingestion to evaluation.
- Image Feature Extraction - Transforms images into high-dimensional feature vectors using a pre-trained neural network.
- Machine Learning Pipelines - Constructs an automated workflow sequencing data preprocessing, model training, and performance evaluation.
- Machine Learning Model Development - Develops a full ML lifecycle including dataset generation, feature extraction, training, and evaluation.
- Genre Co-occurrence Analysis - Analyzes how frequently different movie genres appear together using pairwise co-occurrence matrices.
- Binarized Vector Labeling - Implements binarized vector representation to enable the prediction of multiple genre labels simultaneously.
- Semantic Word Averaging - Converts textual movie descriptions into semantic feature vectors by averaging word embeddings and filtering stop words.
- Label Co-occurrence Analysis - Calculates frequency matrices of category overlaps to visualize genre relationships using heatmaps.
- AI & Machine Learning - Hands-on deep learning project examples.
