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Word embeddings are a type of word representation used in artificial intelligence (AI) and machine learning (ML) to convert words or tokens into numerical vectors. These numerical vectors capture semantic information about words, allowing machines to understand and process human language more effectively. Word embeddings are a crucial component in
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Sequence modeling is a type of machine learning that is used to learn from and predict sequential data. Sequential data is data that has a natural order, such as text, audio, video, and time-series data. Sequence modeling is a subfield of artificial intelligence (AI) and machine learning (ML) that focuses
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Graph Neural Networks (GNNs) are a class of artificial neural networks designed to work with structured data represented as graphs. A graph neural network (GNN) is a type of artificial neural network that is designed to process graph data. Graphs are a powerful way to represent data with relationships between
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A Generative Adversarial Network (GAN) is a class of artificial neural networks used in artificial intelligence (AI) and machine learning (ML) for generative tasks. GANs were introduced by Ian Goodfellow and his colleagues in 2014 and have since become a fundamental and powerful tool in the field of deep learning.
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A Restricted Boltzmann Machine (RBM) is a type of artificial neural network used in artificial intelligence (AI) and machine learning (ML). RBMs are a type of generative stochastic artificial neural network that can learn a probability distribution over its set of inputs. They are particularly well-suited for dimensionality reduction, feature
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A deep belief network (DBN) is a type of artificial neural network that is composed of multiple layers of hidden units, with connections between the layers but not between units within each layer. DBNs are trained to learn a hierarchical representation of the input data, with each layer learning to
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Autoencoders are a class of artificial neural networks used in artificial intelligence (AI) and machine learning (ML) for various purposes, including dimensionality reduction, feature learning, data compression, and generative modeling. They belong to the family of unsupervised learning algorithms and are designed to encode input data into a lower-dimensional representation
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A Recursive Neural Network (RecNN) is a type of neural network architecture used in artificial intelligence (AI) and machine learning (ML) for tasks involving hierarchical or tree-structured data. Unlike traditional feedforward or recurrent neural networks that operate on sequential or grid-like data, RecNNs are designed to process and analyze data
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A Recurrent Neural Network (RNN) is a type of artificial neural network designed for processing sequences of data. Unlike feedforward neural networks, where information flows in one direction from input to output, RNNs have connections that loop back on themselves, allowing them to maintain a hidden state or memory of
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A Convolutional Neural Network (CNN) is a class of deep neural networks designed primarily for processing and analyzing visual data. CNNs are a foundational technology in the fields of artificial intelligence (AI) and machine learning (ML), particularly in computer vision applications. They are highly effective at tasks like image recognition,
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Transformers are a type of architecture in artificial intelligence (AI) and machine learning (ML) that has had a significant impact on various natural language processing (NLP) tasks and other sequence-to-sequence tasks. They were introduced in a 2017 paper titled "Attention Is All You Need" by Vaswani et al.
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A Feedforward Neural Network (FNN), also known as a feedforward neural network model or simply a feedforward network, is a fundamental type of artificial neural network used in machine learning and artificial intelligence. It represents the simplest form of a neural network, where information flows in one direction: from the