A WebGL accelerated JavaScript library for training and deploying ML models.
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Updated
May 18, 2024 - TypeScript
Deep neural networks (DNNs) are a class of artificial neural networks (ANNs) that are deep in the sense that they have many layers of hidden units between the input and output layers. Deep neural networks are a type of deep learning, which is a type of machine learning. Deep neural networks are used in a variety of applications, including speech recognition, computer vision, and natural language processing. Deep neural networks are used in a variety of applications, including speech recognition, computer vision, and natural language processing.
A WebGL accelerated JavaScript library for training and deploying ML models.
An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
(⌐■_■) - Deep Reinforcement Learning instrumenting bettercap for WiFi pwning.
⚡ Automatically decrypt encryptions without knowing the key or cipher, decode encodings, and crack hashes ⚡
BigDL: Distributed TensorFlow, Keras and PyTorch on Apache Spark/Flink & Ray
DeepVariant is an analysis pipeline that uses a deep neural network to call genetic variants from next-generation DNA sequencing data.
Text Classification Algorithms: A Survey
Attempt at tracking states of the arts and recent results (bibliography) on speech recognition.
A deep neural network that learns to drive in video games
An easy to use C# deep learning library with CUDA/OpenCL support
Deep and conventional community detection related papers, implementations, datasets, and tools.
A deep neural network that directly reconstructs the motion of a 3D human skeleton from monocular video [ToG 2020]
Powerful computer vision assisted Lego mosaic creator · Over 1 million images created (so far!)
Convolutional Neural Network for German Traffic Sign Recognition Benchmark
A Pytorch implementation of "Splitter: Learning Node Representations that Capture Multiple Social Contexts" (WWW 2019).
Build logistic regression, neural network models for classification
WB color augmenter improves the accuracy of image classification and image semantic segmentation methods by emulating different WB effects (ICCV 2019) [Python & Matlab].
Going beyond BEDMAP2 using a super resolution deep neural network. Also a convenient flat file data repository for high resolution bed elevation datasets around Antarctica.
Extension that adds support for inferences from pre-built TensorFlow SavedModels
Two-stream CNN for gender classification and biometric identification using a dataset of 11K hand images.