DeepLearningProject

DeepLearningProject - classify genres from te… | Awesome Repos

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.

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Star history

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