Autoencoders
Dimensionality reduction and feature learning
- Autoencoders compress data into a lower-dimensional representation and reconstruct it, useful for anomaly detection.
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Variational Autoencoders
Generative model based on autoencoders
- VAEs generate new data samples by learning probabilistic representations of input data.
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Recommender Systems
Suggest items to users
- Recommender Systems predict user preferences to suggest products, movies, or content.
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Collaborative Filtering
Recommendation based on user behavior
- Collaborative Filtering uses similarities between users or items to make recommendations.
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Content-Based Filtering
Recommendation based on item features
- Content-Based Filtering recommends items similar to those a user has liked based on item attributes.
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Hybrid Recommendation Systems
Combine multiple recommendation approaches
- Hybrid Systems integrate collaborative and content-based filtering for improved recommendations.
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K-Nearest Neighbors
Instance-based learning
- KNN classifies data points based on the majority class of their nearest neighbors.
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Speech Recognition
Convert speech to text
- Speech Recognition enables machines to understand spoken language for applications like voice assistants.
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Decision Trees
Tree-based classification and regression
- Decision Trees split data into branches based on feature values to make predictions.
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