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Deep learning is the subset of machine learning methods based on neural networks with representation learning. The adjective "deep" refers to the use of multiple layers in the network. Methods used can be either supervised, semi-supervised or unsupervised. [2]
Another class of model-free deep reinforcement learning algorithms rely on dynamic programming, inspired by temporal difference learning and Q-learning. In discrete action spaces, these algorithms usually learn a neural network Q-function Q ( s , a ) {\displaystyle Q(s,a)} that estimates the future returns taking action a {\displaystyle a} from ...
v. t. e. Convolutional neural network ( CNN) is a regularized type of feed-forward neural network that learns feature engineering by itself via filters (or kernel) optimization. Vanishing gradients and exploding gradients, seen during backpropagation in earlier neural networks, are prevented by using regularized weights over fewer connections.
Generative artificial intelligence ( generative AI, GenAI, [1] or GAI) is artificial intelligence capable of generating text, images, videos, or other data using generative models, [2] often in response to prompts. [3] [4] Generative AI models learn the patterns and structure of their input training data and then generate new data that has ...
A transformer is a deep learning architecture developed by Google and based on the multi-head attention mechanism, proposed in a 2017 paper "Attention Is All You Need". [1] Text is converted to numerical representations called tokens, and each token is converted into a vector via looking up from a word embedding table. [1]
Recursive neural network. A recursive neural network is a kind of deep neural network created by applying the same set of weights recursively over a structured input, to produce a structured prediction over variable-size input structures, or a scalar prediction on it, by traversing a given structure in topological order.
Diagram of the feature learning paradigm in machine learning for application to downstream tasks, which can be applied to either raw data such as images or text, or to an initial set of features for the data. Feature learning is intended to result in faster training or better performance in task-specific settings than if the data was inputted ...
v. t. e. Multimodal learning, in the context of machine learning, is a type of deep learning using a combination of various modalities of data, such as text, audio, or images, in order to create a more robust model of the real-world phenomena in question. In contrast, singular modal learning would analyze text (typically represented as feature ...
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