Deep Learning: Methods and Applications
Li Deng,Dong Yu +1 more
741
TL;DR: This monograph provides an overview of deep learning methodology and its applications in signal and information processing tasks, including speech recognition, computer vision, natural language processing, and multimodal information processing.
read more
Abstract: This monograph provides an overview of general deep learning methodology and its applications to a variety of signal and information processing tasks. The application areas are chosen with the following three criteria in mind: (1) expertise or knowledge of the authors; (2) the application areas that have already been transformed by the successful use of deep learning technology, such as speech recognition and computer vision; and (3) the application areas that have the potential to be impacted significantly by deep learning and that have been experiencing research growth, including natural language and text processing, information retrieval, and multimodal information processing empowered by multi-task deep learning.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Deep learning in agriculture: A survey
TL;DR: A survey of 40 research efforts that employ deep learning techniques, applied to various agricultural and food production challenges indicates that deep learning provides high accuracy, outperforming existing commonly used image processing techniques.
3.3K
Deep learning and its applications to machine health monitoring
TL;DR: The applications of deep learning in machine health monitoring systems are reviewed mainly from the following aspects: Auto-encoder and its variants, Restricted Boltzmann Machines, Convolutional Neural Networks, and Recurrent Neural Networks.
2.2K
Deep learning for computational biology
Christof Angermueller,Tanel Pärnamaa,Tanel Pärnamaa,Leopold Parts,Leopold Parts,Oliver Stegle +5 more
TL;DR: This review discusses applications of this new breed of analysis approaches in regulatory genomics and cellular imaging, and provides background of what deep learning is, and the settings in which it can be successfully applied to derive biological insights.
Artificial intelligence in digital pathology — new tools for diagnosis and precision oncology
Kaustav Bera,Kurt A. Schalper,David L. Rimm,Vamsidhar Velcheti,Anant Madabhushi,Anant Madabhushi +5 more
TL;DR: A broad framework is provided for incorporating AI and machine learning tools into clinical oncology, with an emphasis on biomarker development, and some of the challenges relating to the use of AI are discussed, including the need for well-curated validation datasets, regulatory approval and fair reimbursement strategies.
A survey on deep learning for big data
TL;DR: The emerging researches of deep learning models for big data feature learning are reviewed and the remaining challenges of big data deep learning are pointed out and the future topics are discussed.
1.1K
References
Deep Residual Learning for Image Recognition
Kaiming He,Xiangyu Zhang,Shaoqing Ren,Jian Sun +3 more
- 27 Jun 2016
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
Distinctive Image Features from Scale-Invariant Keypoints
TL;DR: This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene and can robustly identify objects among clutter and occlusion while achieving near real-time performance.
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
Ross Girshick,Jeff Donahue,Trevor Darrell,Jitendra Malik +3 more
- 23 Jun 2014
TL;DR: RCNN as discussed by the authors combines CNNs with bottom-up region proposals to localize and segment objects, and when labeled training data is scarce, supervised pre-training for an auxiliary task, followed by domain-specific fine-tuning, yields a significant performance boost.
Reducing the Dimensionality of Data with Neural Networks
TL;DR: In this article, an effective way of initializing the weights that allows deep autoencoder networks to learn low-dimensional codes that work much better than principal components analysis as a tool to reduce the dimensionality of data is described.
A fast learning algorithm for deep belief nets
TL;DR: A fast, greedy algorithm is derived that can learn deep, directed belief networks one layer at a time, provided the top two layers form an undirected associative memory.