graph LR
Classification_Clustering_Models["Classification & Clustering Models"]
Deep_Learning_Models_Feedforward_CNN_["Deep Learning Models (Feedforward & CNN)"]
Generative_Models["Generative Models"]
Recommender_Systems["Recommender Systems"]
Natural_Language_Processing_NLP_Models["Natural Language Processing (NLP) Models"]
Dimensionality_Reduction_Models["Dimensionality Reduction Models"]
Model_Utilities_Supplementaries["Model Utilities & Supplementaries"]
Classification_Clustering_Models -- "uses" --> Model_Utilities_Supplementaries
Deep_Learning_Models_Feedforward_CNN_ -- "uses" --> Model_Utilities_Supplementaries
Generative_Models -- "uses" --> Model_Utilities_Supplementaries
Recommender_Systems -- "uses" --> Model_Utilities_Supplementaries
Natural_Language_Processing_NLP_Models -- "uses" --> Model_Utilities_Supplementaries
Dimensionality_Reduction_Models -- "uses" --> Model_Utilities_Supplementaries
The Core ML Model Repository subsystem is primarily defined by the libra.query package, specifically encompassing modules responsible for various machine learning model implementations and their execution. Its boundaries are marked by the following key modules: libra.query.classification_models, libra.query.feedforward_nn, libra.query.generative_models, libra.query.recommender_systems, libra.query.nlp_queries, libra.query.dimensionality_red_queries, libra.query.supplementaries (as a supporting utility component).
Encapsulates traditional machine learning algorithms for classification and clustering tasks, including K-Means, SVM, Nearest Neighbors, Decision Trees, and XGBoost. This component is responsible for executing these specific model types.
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Manages and executes deep learning models, specifically feedforward and convolutional neural networks, for both regression and classification problems. It handles the computational aspects of these neural network architectures.
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Implements and orchestrates the training and execution of generative models, with a focus on Deep Convolutional Generative Adversarial Networks (DCGANs). This component handles the complex training loops and generation processes.
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Provides functionality for content-based recommendation systems. It is responsible for processing user and item data to generate relevant recommendations.
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Implements and executes various Natural Language Processing tasks, such as text classification and summarization. This component handles the specific algorithms and data transformations required for NLP.
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Manages and applies algorithms for dimensionality reduction, which are crucial for preprocessing high-dimensional data before feeding it into other ML models.
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Offers auxiliary functions critical for the operation of all other model components. This includes functionalities for model tuning, saving/loading models, data retrieval specific to model needs, and plot generation for visualization of results or model performance.
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