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- Self-Supervised Learning Description: Self-supervised learning is an approach within machine learning that allows models to learn from unlabeled data by generating their(...) Read more
- SVM Description: SVM, short for Support Vector Machine, is a supervised learning model primarily used for classification and regression. Its goal is(...) Read more
- Softmax Description: Softmax is a mathematical function that transforms a vector of real numbers into a vector of probabilities, where each value is in(...) Read more
- Similarity Learning Description: Similarity Learning is an approach within machine learning that focuses on learning a function that measures the similarity between(...) Read more
- Supervised Feature Selection Description: Supervised feature selection is a fundamental process in machine learning that involves identifying and selecting a subset of(...) Read more
- Sequential Learning Description: Sequential learning is an approach within machine learning where models are trained using data presented in a temporal sequence.(...) Read more
- Supervised Learning Algorithm Description: A supervised learning algorithm is a method within the field of machine learning that uses labeled data to learn how to map inputs(...) Read more
- Siamese Network Description: The Siamese Network is a neural network architecture designed to learn to differentiate between two input samples by comparing(...) Read more
- Sparse Neural Networks Description: Sparse Neural Networks are a type of neural network architecture characterized by having a significant number of weights that are(...) Read more
- Spatial Attention Description: Spatial Attention is a fundamental mechanism in the field of neural networks, especially in deep learning and convolutional neural(...) Read more
- Scale Invariance Description: Scale invariance is a fundamental property in the field of computer vision and convolutional neural networks (CNNs). It refers to(...) Read more
- Sequence Modeling Description: Sequence modeling is an approach within deep learning that focuses on predicting the next element in a sequence based on previous(...) Read more
- Sparse Autoencoder Description: A sparse autoencoder is a type of neural network used in deep learning to learn efficient representations of input data by imposing(...) Read more
- Spatial Transformer Network Description: Spatial Transformer Networks are a type of neural network architecture that specializes in performing spatial transformations on(...) Read more
- Spectral Clustering Description: Spectral Clustering is an unsupervised learning technique based on graph theory and spectral analysis for grouping data. It uses(...) Read more
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