All Listings

Autoencoders

Dimensionality reduction and feature learning

Autoencoders compress data into a lower-dimensional representation and reconstruct it, useful for anomaly detection.

Generative model based on autoencoders

VAEs generate new data samples by learning probabilistic representations of input data.

Suggest items to users

Recommender Systems predict user preferences to suggest products, movies, or content.

Recommendation based on user behavior

Collaborative Filtering uses similarities between users or items to make recommendations.

Recommendation based on item features

Content-Based Filtering recommends items similar to those a user has liked based on item attributes.

Combine multiple recommendation approaches

Hybrid Systems integrate collaborative and content-based filtering for improved recommendations.

Instance-based learning

KNN classifies data points based on the majority class of their nearest neighbors.

Convert speech to text

Speech Recognition enables machines to understand spoken language for applications like voice assistants.

Tree-based classification and regression

Decision Trees split data into branches based on feature values to make predictions.