Spandan-Madan/DeepLearningProject
DeepLearningProject
This project is a multi-label classification pipeline designed for genre prediction. It implements a machine learning workflow that assigns multiple category labels to a single item by processing both textual and visual input data.
The system utilizes multimodal feature extraction to transform images and text descriptions into semantic vectors. This process includes using pre-trained networks for visual feature extraction and semantic word averaging for text analysis, allowing the model to integrate different data types into a unified input.
The pipeline covers the full machine learning lifecycle, including dataset metadata integration from external databases and the organization of data into a multi-stage linear pipeline. Performance is measured through ground-truth metric evaluation using precision and recall calculations, while category relationships are analyzed through pairwise co-occurrence matrices.
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.
Star history
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