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A convolutional neural network (CNN) is a regularized type of feed-forward neural network that learns features by itself via filter (or kernel) optimization. This type of deep learning network has been applied to process and make predictions from many different types of data including text, images and audio. [ 1 ]
A February 2016 paper from his lab exploring convolutional neural network architectures and transfer learning for lymph node detection and interstitial lung disease classification had over 1,000 citations as of early 2019. [8] In 2018 he was the keynote speaker at the inaugural Medical Imaging and Deep Learning (MIDL) conference. [9]
Neural networks, such as recurrent neural networks (RNN), convolutional neural networks (CNN), and Hopfield neural networks have been added. [46] For example, in 2018, Fioravanti et al. developed an algorithm called Ph-CNN to classify data samples from healthy patients and patients with IBD symptoms (to distinguish healthy and sick patients) by ...
Website with research, news, and perspectives bout security topics. This data is not pre-processed Reviewed list of Trendmicro research, news, and perspectives. [380] The Hacker News News about cybersecurity topics. This data is not pre-processed data breaches, cyberattacks, vulnerabilities, malware news. [381] Krebsonsecurity
This is a list of datasets for machine learning research. It is part of the list of datasets for machine-learning research. These datasets consist primarily of images or videos for tasks such as object detection, facial recognition, and multi-label classification.
Microsoft Cognitive Toolkit (previously known as CNTK), an open source toolkit for building artificial neural networks. [82] OpenNN, a comprehensive C++ library implementing neural networks. [83] PyTorch, an open-source Tensor and Dynamic neural network in Python. [84] TensorFlow, an open-source software library for machine learning. [85]
LOCtarget is a tool for predicting, and a database of pre-computed predictions for, sub-cellular localization of eukaryotic and prokaryotic proteins. Several methods are employed to make the predictions, including text analysis of SWISS-PROT keywords, nuclear localization signals, and the use of neural networks. (bio.tools entry) [41]
The core of DeepVariant [19] is a convolutional neural network (CNN) that identifies variants by transforming this task into an image classification operation. In Parabricks, the inference process is accelerated in hardware. For this pipeline, only T4, V100, and A100 GPUs are supported. [13]