Search results
Results From The WOW.Com Content Network
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]
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. The adjective "deep" refers to the use of multiple layers in the network.
AlexaTM 20B achieved state-of-the-art performance in few-shot-learning tasks across all Flores-101 language pairs, outperforming GPT-3 on several tasks. Architecture. A seq2seq model is composed of an encoder and a decoder that typically implemented as RNNs. The encoder captures the context of the input sequence and sends it to the decoder ...
One-hot. In digital circuits and machine learning, a one-hot is a group of bits among which the legal combinations of values are only those with a single high (1) bit and all the others low (0). [1] A similar implementation in which all bits are '1' except one '0' is sometimes called one-cold. [2] In statistics, dummy variables represent a ...
t. e. An autoencoder is a type of artificial neural network used to learn efficient codings of unlabeled data ( unsupervised learning ). [1] [2] An autoencoder learns two functions: an encoding function that transforms the input data, and a decoding function that recreates the input data from the encoded representation.
A feedforward neural network (FNN) is one of the two broad types of artificial neural network, characterized by direction of the flow of information between its layers. Its flow is uni-directional, meaning that the information in the model flows in only one direction—forward—from the input nodes, through the hidden nodes (if any) and to the output nodes, without any cycles or loops, in ...
Hidden Markov model. A hidden Markov model ( HMM) is a Markov model in which the observations are dependent on a latent (or "hidden") Markov process (referred to as ). An HMM requires that there be an observable process whose outcomes depend on the outcomes of in a known way.
In machine learning and pattern recognition, a feature is an individual measurable property or characteristic of a phenomenon. [1] Choosing informative, discriminating and independent features is a crucial element of effective algorithms in pattern recognition, classification and regression. Features are usually numeric, but structural features ...