… We will focus on modern DL, the ongoing trends and future … In this article, we reviewed
some selected modern and … future applications of modern DL methods in bioinformatics. …

… This article surveys recent developments in deep learning based object detectors. Concise
overview of benchmark datasets and evaluation metrics used in detection is also provided …

… The earliest predecessors of modern deep learning were simple linear models motivated
from a neuroscientific perspective. These models were designed to take a set of n input values …

… Whereas a decade ago most AI research could be performed on a commodity desktop
computer, modern deep learning research increasingly requires access to a cluster containing …

… of the mathematical analysis of deep learning. This field emerged around a list of research
questions that were not answered within the classical framework of learning theory. These …

… This seemed to suggest that advances in exploiting modern computing hardware were …
Thus LSTM variants could learn previously unlearnable Very Deep Learning tasks (including …

… the deep learning community. … deep learning workloads for study. Each of these models
comes from a seminal work in the deep learning community, ranging from the familiar deep …

… We find that majority of the recent publications have leveraged deep learning to enhance …
the specific deep learning techniques used for it and a brief discussion of how deep learning …

… Compared to other machine learning (ML) algorithms, deep neural networks (DNN) provide
… This paper briefly discusses the use of deep learning (DL) for recognizing fruits and its other …

… deep learning … of deep learning—stochastic approximation methods and normalisation
layers—and make recommendations for how to better adapt this classic method to the modern …