![Dynamic gesture recognition based on 2D convolutional neural network and feature fusion | Scientific Reports Dynamic gesture recognition based on 2D convolutional neural network and feature fusion | Scientific Reports](https://media.springernature.com/full/springer-static/image/art%3A10.1038%2Fs41598-022-08133-z/MediaObjects/41598_2022_8133_Fig1_HTML.png)
Dynamic gesture recognition based on 2D convolutional neural network and feature fusion | Scientific Reports
![Automatic classification and segmentation of single-molecule fluorescence time traces with deep learning | Nature Communications Automatic classification and segmentation of single-molecule fluorescence time traces with deep learning | Nature Communications](https://media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-020-19673-1/MediaObjects/41467_2020_19673_Fig1_HTML.png)
Automatic classification and segmentation of single-molecule fluorescence time traces with deep learning | Nature Communications
![Enabling Damage Identification of Structures Using Time Series–Based Feature Extraction Algorithms | Journal of Aerospace Engineering | Vol 32, No 3 Enabling Damage Identification of Structures Using Time Series–Based Feature Extraction Algorithms | Journal of Aerospace Engineering | Vol 32, No 3](https://ascelibrary.org/cms/asset/6d97a578-0799-478c-94fa-1b6a894e8ca5/figure1.gif)
Enabling Damage Identification of Structures Using Time Series–Based Feature Extraction Algorithms | Journal of Aerospace Engineering | Vol 32, No 3
![The Hitchhiker's Guide to Feature Extraction | Deep learning, Dimensionality reduction, Feature extraction The Hitchhiker's Guide to Feature Extraction | Deep learning, Dimensionality reduction, Feature extraction](https://i.pinimg.com/736x/0b/a6/7a/0ba67a8b5ff61099f5de3544b003a445.jpg)
The Hitchhiker's Guide to Feature Extraction | Deep learning, Dimensionality reduction, Feature extraction
![Autonomous extraction of millimeter-scale deformation in InSAR time series using deep learning | Nature Communications Autonomous extraction of millimeter-scale deformation in InSAR time series using deep learning | Nature Communications](https://media.springernature.com/full/springer-static/image/art%3A10.1038%2Fs41467-021-26254-3/MediaObjects/41467_2021_26254_Fig1_HTML.png)
Autonomous extraction of millimeter-scale deformation in InSAR time series using deep learning | Nature Communications
![Natural-language Processing Feature Extraction Bag-of-words Model Unified Medical Language System PNG, Clipart, Angle, Natural-language Processing Feature Extraction Bag-of-words Model Unified Medical Language System PNG, Clipart, Angle,](https://cdn.imgbin.com/19/20/10/imgbin-natural-language-processing-feature-extraction-bag-of-words-model-unified-medical-language-system-others-hyRV2MBeHBJ3DbJb9gWcRwHSK.jpg)
Natural-language Processing Feature Extraction Bag-of-words Model Unified Medical Language System PNG, Clipart, Angle,
![Evolutionarily informed machine learning enhances the power of predictive gene-to-phenotype relationships | Nature Communications Evolutionarily informed machine learning enhances the power of predictive gene-to-phenotype relationships | Nature Communications](https://media.springernature.com/full/springer-static/image/art%3A10.1038%2Fs41467-021-25893-w/MediaObjects/41467_2021_25893_Fig1_HTML.png)
Evolutionarily informed machine learning enhances the power of predictive gene-to-phenotype relationships | Nature Communications
![Deep learning encodes robust discriminative neuroimaging representations to outperform standard machine learning | Nature Communications Deep learning encodes robust discriminative neuroimaging representations to outperform standard machine learning | Nature Communications](https://media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-020-20655-6/MediaObjects/41467_2020_20655_Fig1_HTML.png)
Deep learning encodes robust discriminative neuroimaging representations to outperform standard machine learning | Nature Communications
![Feature extraction and classification of climate change risks: a bibliometric analysis | SpringerLink Feature extraction and classification of climate change risks: a bibliometric analysis | SpringerLink](https://media.springernature.com/lw685/springer-static/image/art%3A10.1007%2Fs10661-022-10074-z/MediaObjects/10661_2022_10074_Fig1_HTML.png)
Feature extraction and classification of climate change risks: a bibliometric analysis | SpringerLink
![A deep-learning system bridging molecule structure and biomedical text with comprehension comparable to human professionals | Nature Communications A deep-learning system bridging molecule structure and biomedical text with comprehension comparable to human professionals | Nature Communications](https://media.springernature.com/m685/springer-static/image/art%3A10.1038%2Fs41467-022-28494-3/MediaObjects/41467_2022_28494_Fig1_HTML.png)
A deep-learning system bridging molecule structure and biomedical text with comprehension comparable to human professionals | Nature Communications
![Deep Reader: Information extraction from Document images via relation extraction and Natural Language: Paper and Code - CatalyzeX Deep Reader: Information extraction from Document images via relation extraction and Natural Language: Paper and Code - CatalyzeX](https://ai2-s2-public.s3.amazonaws.com/figures/2017-08-08/526c4dcd7e4e873fd58c807e2c5959ec5647e562/5-Figure1-1.png)
Deep Reader: Information extraction from Document images via relation extraction and Natural Language: Paper and Code - CatalyzeX
![Cross-property deep transfer learning framework for enhanced predictive analytics on small materials data | Nature Communications Cross-property deep transfer learning framework for enhanced predictive analytics on small materials data | Nature Communications](https://media.springernature.com/full/springer-static/image/art%3A10.1038%2Fs41467-021-26921-5/MediaObjects/41467_2021_26921_Fig1_HTML.png)
Cross-property deep transfer learning framework for enhanced predictive analytics on small materials data | Nature Communications
![A robust and interpretable machine learning approach using multimodal biological data to predict future pathological tau accumulation | Nature Communications A robust and interpretable machine learning approach using multimodal biological data to predict future pathological tau accumulation | Nature Communications](https://media.springernature.com/full/springer-static/image/art%3A10.1038%2Fs41467-022-28795-7/MediaObjects/41467_2022_28795_Fig1_HTML.png)
A robust and interpretable machine learning approach using multimodal biological data to predict future pathological tau accumulation | Nature Communications
![Natural Language Processing IT Rule Based NLP Machine Learning Based NLP And Deep Learning | Presentation Graphics | Presentation PowerPoint Example | Slide Templates Natural Language Processing IT Rule Based NLP Machine Learning Based NLP And Deep Learning | Presentation Graphics | Presentation PowerPoint Example | Slide Templates](https://www.slideteam.net/media/catalog/product/cache/1280x720/n/a/natural_language_processing_it_rule_based_nlp_machine_learning_based_nlp_and_deep_learning_slide01.jpg)
Natural Language Processing IT Rule Based NLP Machine Learning Based NLP And Deep Learning | Presentation Graphics | Presentation PowerPoint Example | Slide Templates
![Feature Extraction and Analysis of Natural Language Processing for Deep Learning English Language | Semantic Scholar Feature Extraction and Analysis of Natural Language Processing for Deep Learning English Language | Semantic Scholar](https://d3i71xaburhd42.cloudfront.net/6db254ee21104f5a6b5d53c600cf5d3b9903ae3f/6-Figure3-1.png)
Feature Extraction and Analysis of Natural Language Processing for Deep Learning English Language | Semantic Scholar
![A generalizable and accessible approach to machine learning with global satellite imagery | Nature Communications A generalizable and accessible approach to machine learning with global satellite imagery | Nature Communications](https://media.springernature.com/full/springer-static/image/art%3A10.1038%2Fs41467-021-24638-z/MediaObjects/41467_2021_24638_Fig1_HTML.png)
A generalizable and accessible approach to machine learning with global satellite imagery | Nature Communications
![Feature Extraction Techniques. An end to end guide on how to reduce a… | by Pier Paolo Ippolito | Towards Data Science Feature Extraction Techniques. An end to end guide on how to reduce a… | by Pier Paolo Ippolito | Towards Data Science](https://miro.medium.com/max/1234/1*dn6881AU9C06if2SZJZRag.gif)
Feature Extraction Techniques. An end to end guide on how to reduce a… | by Pier Paolo Ippolito | Towards Data Science
![Peak learning of mass spectrometry imaging data using artificial neural networks | Nature Communications Peak learning of mass spectrometry imaging data using artificial neural networks | Nature Communications](https://media.springernature.com/full/springer-static/image/art%3A10.1038%2Fs41467-021-25744-8/MediaObjects/41467_2021_25744_Fig1_HTML.png)
Peak learning of mass spectrometry imaging data using artificial neural networks | Nature Communications
![Human-interpretable image features derived from densely mapped cancer pathology slides predict diverse molecular phenotypes | Nature Communications Human-interpretable image features derived from densely mapped cancer pathology slides predict diverse molecular phenotypes | Nature Communications](https://media.springernature.com/full/springer-static/image/art%3A10.1038%2Fs41467-021-21896-9/MediaObjects/41467_2021_21896_Fig1_HTML.png)