The neurons between the input and output layers of a neural network are referred to as hidden layers. The term “deep” usually refers to the number of hidden layers in the neural network. Deep learning models can have hundreds or even thousands of hidden layers....
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3. literary term for an ocean; "denizens of thedeep" Adjective 1. relatively deep or strong; affecting one deeply; "adeepbreath" "adeepsigh" "deepconcentration" "deepemotion" "adeeptrance" "in adeepsleep" 2. marked by depth of thinking; ...
Deep learning enables a computer to learn by example. To understand deep learning, imagine a toddler whose first word isdog. The toddler learns what a dog is -- and is not -- by pointing to objects and saying the worddog. The parent says, "Yes, that is a dog," or "No, that isn...
allowing for quantification of uncertainty of neural network parameters and easy Bayesian inference via probabilistic programming and automated variational inference. In the longer term, there might be a reduced modeling vocabulary that nails the salient properties that a deep net can have and thus reduc...
The word "TP" refers to a successful detection of true instants. The abbreviation "TN" denotes a result in which the proposed system correctly recognized the kind of fruit that was misclassified. The term "FP" refers to a situation in which the suggested framework incorrectly identified a ...
Natural language processing (NLP) transformers provide remarkable power since they can run in parallel, processing multiple portions of a sequence simultaneously, which then greatly speeds training. Transformers also track long-term dependencies in text, which enables them to understand the overall context...
The term “deep learning” appears to presume that other kinds of machine-learning activities are “shallow.” This is not the case. The previous chapters have exposed you to some very sophisticated methods in predictive analytics (e.g., lag variables for time-series analysis and ensembles of ...
Regularizationis the general statistical term for a mathematical operation that limits memorization while promoting generalizable learning. There are many different types of regularization available, which we will cover in the next few sections.
Layer Type Layer Name Vision layer Convolution, Depthwise convolution, Deconvolution, Pooling (Max/Average/Global Average), Upsample, Focus 1 Recurrent layer LSTM (Long Short-Term Memory) Normalization Batch Normalization, Scale Activation ReLU, ReLU6, Sigmoid, H-Swish, SiLU 1 Utility Eltwise (Sum/...