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https://opensea.io/collection/dev-21
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Pablo
@firsttimecrypto
Unsupervised Learning Description: The model learns patterns and structures in data without labeled outputs. It identifies hidden relationships or clusters in the data. Common Use Cases: Customer segmentation. Anomaly detection. Recommendation systems. Examples: Clustering (e.g., K-Means, DBSCAN) Dimensionality Reduction (e.g., PCA, t-SNE) Generative Models (e.g., GANs for generating new data) Semi-Supervised Learning Description: Combines a small amount of labeled data with a large amount of unlabeled data to improve learning accuracy. Common Use Cases: Image recognition tasks with limited labeled images. Natural Language Processing (NLP) with partially labeled text. Examples: Self-training algorithms. Graph-based learning.
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