Apr 10, 2019 · We highlight new characteristics of deep learning (including depth and over-parametrization) and explain their practical and theoretical benefits.
Apr 19, 2020 · Deep learning uses the composition of many nonlinear functions to model the complex dependency between input features and labels.
We highlight new characteristics of deep learning (including depth and overparametrization) and explain their practical and theoretical benefits.
A Selective Overview of Deep Learning - Princeton University
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Along the way, we highlight new characteristics of deep learning (including depth and overparametrization) and explain their practical and theoretical benefits.
This work introduces common neural network models and training techniques from a statistical point of view and highlights new characteristics of deep ...
Comprehensive overview of key concepts and recent advancements in deep learning, covering neural network models, training techniques, and theoretical ...
In simple words, deep learning uses the composition of many nonlinear functions to model the complex dependency between input features and labels. While neural ...
(PDF) A Selective Overview of Deep Learning - ResearchGate
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Apr 30, 2019 · In simple words, deep learning uses the composition of many nonlinear functions to model the complex dependency between input features and ...
A Selective Overview of Deep Learning. https://doi.org/10.1214/20-sts783 ·. Journal: Statistical Science, 2021, № 2. Publisher: Institute of Mathematical ...
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ABSTRACT: The latest advances in Deep Learning based methods and computational capabilities provide new opportunities for vehicle tracking. In this study, ...