# spandan-madan/deeplearningproject **Attribution required: if you use, quote, or summarise this content, you must credit and link back to [awesome-repositories.com](https://awesome-repositories.com/repository/spandan-madan-deeplearningproject).** 4,785 stars · 634 forks · HTML · MIT ## Links - GitHub: https://github.com/Spandan-Madan/DeepLearningProject - Homepage: https://spandan-madan.github.io/DeepLearningProject/ - awesome-repositories: https://awesome-repositories.com/repository/spandan-madan-deeplearningproject.md ## Topics `deep-learning` `machine-learning` `neural-networks` `tutorial` ## Description 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. ## Tags ### Artificial Intelligence & ML - [Visual-Language Multimodal Integration](https://awesome-repositories.com/f/artificial-intelligence-ml/visual-language-multimodal-integration.md) — Combines visual features from images and semantic vectors from text into a unified input for genre prediction. - [Lexical Feature Extractions](https://awesome-repositories.com/f/artificial-intelligence-ml/feature-extraction/lexical-feature-extractions.md) — Extracts textual features from descriptions using word embedding averaging and stop-word removal. ([source](https://spandan-madan.github.io/DeepLearningProject/)) - [Multimodal Feature Extractors](https://awesome-repositories.com/f/artificial-intelligence-ml/feature-extraction/multimodal-feature-extractors.md) — Implements multimodal feature extraction to combine image and text data into unified vectors for genre classification. - [Ground-Truth Scoring](https://awesome-repositories.com/f/artificial-intelligence-ml/ground-truth-scoring.md) — Evaluates model success by comparing predicted category vectors against original labels using precision and recall. - [Machine Learning Pipelines](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning-pipelines.md) — Builds a complete machine learning workflow from custom data generation to deep learning model execution. ([source](https://cdn.jsdelivr.net/gh/spandan-madan/deeplearningproject@master/README.md)) - [Model Evaluation Metrics](https://awesome-repositories.com/f/artificial-intelligence-ml/model-evaluation-metrics.md) — Evaluates classification accuracy using precision and recall metrics compared to ground truth labels. - [Model Performance Evaluators](https://awesome-repositories.com/f/artificial-intelligence-ml/model-performance-evaluators.md) — Measures prediction accuracy by comparing predicted labels against ground-truth data using precision and recall. ([source](https://spandan-madan.github.io/DeepLearningProject/)) - [Semantic Word Averaging](https://awesome-repositories.com/f/artificial-intelligence-ml/natural-language-processing/word-embeddings/semantic-word-averaging.md) — Converts textual plot descriptions into feature vectors by averaging word embeddings after filtering stop words. - [Pre-trained Neural Feature Extraction](https://awesome-repositories.com/f/artificial-intelligence-ml/text-feature-extraction/pre-trained-neural-feature-extraction.md) — Transforms raw images and text into high-dimensional vectors using pre-trained network layers. - [Machine Learning Model Development](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning-model-development.md) — Develops a full ML lifecycle including dataset generation, feature extraction, training, and evaluation. - [Genre Co-occurrence Analysis](https://awesome-repositories.com/f/artificial-intelligence-ml/music-genre-classifiers/genre-co-occurrence-analysis.md) — Analyzes how frequently different movie genres appear together using pairwise co-occurrence matrices. ([source](https://spandan-madan.github.io/DeepLearningProject/)) ### Data & Databases - [Multi-Label Classifiers](https://awesome-repositories.com/f/data-databases/data-categorization/classification-labelers/multi-label-classifiers.md) — Implements a machine learning pipeline that assigns multiple category labels to a single item. - [Genre Prediction](https://awesome-repositories.com/f/data-databases/data-categorization/classification-labelers/multi-label-classifiers/genre-prediction.md) — Trains neural networks to assign multiple genre labels based on textual and visual features. - [Multi-Label Prediction Analysis](https://awesome-repositories.com/f/data-databases/data-categorization/classification-labelers/multi-label-classifiers/multi-label-prediction-analysis.md) — Assigns multiple genre labels to items using a neural network trained on binarized feature vectors. ([source](https://spandan-madan.github.io/DeepLearningProject/)) - [Binarized Vector Labeling](https://awesome-repositories.com/f/data-databases/data-categorization/classification-labelers/multi-label-classifiers/multi-label-prediction-analysis/binarized-vector-labeling.md) — Implements binarized vector representation to enable the prediction of multiple genre labels simultaneously. - [Semantic Word Averaging](https://awesome-repositories.com/f/data-databases/vector-storage/text-vectorizers/semantic-word-embeddings/semantic-word-averaging.md) — Converts textual movie descriptions into semantic feature vectors by averaging word embeddings and filtering stop words. ### DevOps & Infrastructure - [Configurable Stage Pipelines](https://awesome-repositories.com/f/devops-infrastructure/cli-job-runners/multi-stage-pipeline-orchestrators/configurable-stage-pipelines.md) — Organizes the machine learning workflow into a sequential multi-stage pipeline from ingestion to evaluation. ### Graphics & Multimedia - [Image Feature Extraction](https://awesome-repositories.com/f/graphics-multimedia/image-feature-extraction.md) — Transforms images into high-dimensional feature vectors using a pre-trained neural network. ([source](https://spandan-madan.github.io/DeepLearningProject/)) ### Software Engineering & Architecture - [Machine Learning Pipelines](https://awesome-repositories.com/f/software-engineering-architecture/pipeline-automation/machine-learning-pipelines.md) — Constructs an automated workflow sequencing data preprocessing, model training, and performance evaluation. ### Education & Learning Resources - [Label Co-occurrence Analysis](https://awesome-repositories.com/f/education-learning-resources/educational-resources/systems-applied-computing/machine-learning-education/computational-performance-optimization/pairwise-interaction-analysis/label-co-occurrence-analysis.md) — Calculates frequency matrices of category overlaps to visualize genre relationships using heatmaps. ### Part of an Awesome List - [AI & Machine Learning](https://awesome-repositories.com/f/awesome-lists/ai/ai-machine-learning.md) — Hands-on deep learning project examples.