Deep Learning

@article{Goodfellow2015DeepL,
  title={Deep Learning},
  author={Ian J. Goodfellow and Yoshua Bengio and Aaron C. Courville},
  journal={Nature},
  year={2015},
  volume={521},
  pages={436-444}
}
Machine-learning technology powers many aspects of modern society: from web searches to content filtering on social networks to recommendations on e-commerce websites, and it is increasingly present in consumer products such as cameras and smartphones. [] Key Method Representation learning is a set of methods that allows a machine to be fed with raw data and to automatically discover the representations needed for detection or classification.
Neural Networks for Survey Researchers
TLDR
This article describes what neural networks are and how they learn, considers their strengths and weaknesses as a machine learning approach, and illustrates how they perform on a classification task predicting survey response from respondents’ (and nonrespondents’) prior known demographics.
Deep Learning: A Primer for Radiologists.
  • G. ChartrandP. Cheng A. Tang
  • Computer Science
    Radiographics : a review publication of the Radiological Society of North America, Inc
  • 2017
TLDR
The key concepts of deep learning for clinical radiologists are reviewed, technical requirements are discussed, emerging applications in clinical radiology are described, and limitations and future directions in this field are outlined.
Deep learning: a branch of machine learning
TLDR
A broad writing survey is completed and the utilization of deep learning in different fields is reviewed and how and in what real applications deep learning algorithms have been used are shown.
A Review of Deep Learning Algorithms and Their Applications in Healthcare
TLDR
A review and a checkpoint to systemize the popular algorithms of deep learning and to encourage further innovation regarding their applications and to introduce detailed information on how to apply several deep learning algorithms in healthcare, such as in relation to the COVID-19 pandemic.
A Detailed Survey on Deep Learning Techniques for Real-Time Image Classification, Recognition and Analysis
TLDR
This paper is going to address deep learning techniques such as single-shot detector (SSD), scale-invariant feature transform (sfit), histogram of oriented gradient (HOG) and many more to detect cybercrimes through the assistance of the above-mentioned techniques.
Tricks from Deep Learning
TLDR
A way to dramatically reduce the size of the tape when performing reverse-mode AD on a (theoretically) time-reversible process like an ODE integrator; and a new mathematical insight that allows for the implementation of a stochastic Newton's method are discussed.
Convolutional neural network and its pretrained models for image classification and object detection: A survey
TLDR
This paper presents detailed and analytical literature starting from the very elementary level to the recent trends of this trending technology while focusing on the most used DL model, that is, convolutional neural network and its pretrained models for image classification and object detection.
Recent advances in deep learning
TLDR
Focusing on recent developments in DL architectures and their applications, the articles in this issue are classified into four categories: (1) deep architectures and conventional neural networks, (2) incremental learning, (3) recurrent neural Networks, and (4) generative models and adversarial examples.
Deep learning in the automotive industry: Applications and tools
TLDR
An end-to-end deep learning application utilizing a mobile app for data collection and process support, and an Amazon-based cloud backend for storage and training, and the effectiveness of the trained classifier in a real world setting during manufacturing process is demonstrated.
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