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give me information about deep learning

5 cited papers · March 29, 2026 · Powered by Researchly AI

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Deep learning allows computational models composed of multiple processing layers to learn representations of data with multiple levels of abstraction. Le The fi…

Deep learning allows computational models composed of multiple processing layers to learn representations of data with multiple levels of abstraction.1LeCun et al. (2015)1The field has grown rapidly and has been extensively used to address a wide range of applications, outperforming well-known machine learning techniques in many domains including cybersecurity, natural language processing, bioinformatics, robotics, and medical information processing.2Alzubaidi et al. (2021)2
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Deep LearningYann LeCun, Yoshua Bengio et al.2015Nature
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Review of deep learning: concepts, CNN architectures, challenges, applications, future directionsLaith Alzubaidi, Jinglan Zhang et al.2021Journal Of Big Data
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  • Deep Neural Network (DNN) — Refers to Artificial Neural Networks (ANN) with multiple layers, capable of handling huge amounts of data and surpassing classical methods in pattern recognition.
1Albawi et al. (2017)1
  • Convolutional Neural Network (CNN) — One of the most popular deep neural network architectures, consisting of convolutional, non-linearity, pooling, and fully-connected layers, with excellent performance on image data and NLP tasks.
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  • Recurrent Neural Network (RNN) — An architecture widely used for time sequence-dependent problems, with gated variants such as LSTM and GRU making significant improvements in sequential input tasks. Nosouhian et al. (2021)
  • Data Augmentation — A data-space solution to the problem of limited training data, encompassing techniques such as geometric transformations, color space augmentations, GANs, and neural style transfer to enhance dataset size and quality.
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Understanding of a convolutional neural networkSaad Albawi, Tareq Abed Mohammed et al.2017OpenAlex
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A survey on Image Data Augmentation for Deep LearningConnor Shorten, Taghi M. Khoshgoftaar2019Journal Of Big Data
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Diagram
Input Data
 |
 v
[Preprocessing / Feature Extraction]
 |
 v
[Deep Neural Network Layers]
 |--- Convolutional Layers (CNNs)
 |--- Recurrent Layers (RNN / LSTM / GRU)
 |--- Fully Connected Layers
 |
 v
[Activation / Non-linearity]
 |
 v
[Output Layer]
 |
 v
Prediction / Classification / Generation
Table
ArchitectureBest ForKey Feature
CNNImage classification, computer vision, NLPConvolutional + pooling layers; spatial feature extraction
RNN / LSTM / GRUSequential/time-series data, NLPHandles temporal dependencies; gated memory units
Multi-column DNNImage classification benchmarksMultiple deep columns averaged for prediction
Deep learning has dramatically improved the state of the art in speech recognition, visual object recognition, and object detection.1Multi-column deep neural network architectures have achieved near-human performance on handwriting benchmarks and outperformed humans on traffic sign recognition by a factor of two.23

Cireşan et al. (2012)

1
Deep LearningYann LeCun, Yoshua Bengio et al.2015Nature
View
2
Review of deep learning: concepts, CNN architectures, challenges, applications, future directionsLaith Alzubaidi, Jinglan Zhang et al.2021Journal Of Big Data
View
3
Understanding of a convolutional neural networkSaad Albawi, Tareq Abed Mohammed et al.2017OpenAlex
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  • The remote sensing and other specialized communities face unique challenges including inadequate datasets, the need for human-understandable solutions, big data management, and nontraditional heterogeneous data sources.
1

Ball et al. (2017)

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Comprehensive survey of deep learning in remote sensing: theories, tools, and challenges for the communityJohn E. Ball, Derek T. Anderson et al.2017Journal of Applied Remote Sensing
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  • Deep learning enables models to learn multi-level data representations, revolutionizing fields like speech recognition and object detection.
1
  • CNNs are among the most powerful and widely used architectures, especially for image-related tasks.
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  • RNN variants like LSTM and GRU are the go-to architectures for sequential and time-dependent problems, with multi-layer GRU showing superior performance in some tasks.
  • Deep learning has matched or exceeded human performance across numerous complex cognitive tasks.
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1
Deep LearningYann LeCun, Yoshua Bengio et al.2015Nature
View
2
Understanding of a convolutional neural networkSaad Albawi, Tareq Abed Mohammed et al.2017OpenAlex
View
3
Review of deep learning: concepts, CNN architectures, challenges, applications, future directionsLaith Alzubaidi, Jinglan Zhang et al.2021Journal Of Big Data
View
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  1. "Comparison of CNN architectures: AlexNet, VGG, ResNet, and Inception for image classification"
  2. "LSTM vs GRU for natural language processing and time series forecasting"
  3. "Transfer learning techniques in deep learning for limited data scenarios"

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