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It is the replication of the code in simpler terms available on GitHub.
Increase image resolution by autoencoder
Semantic Image Search with Convolutional Neural Networks
For data alignment, different data record granularity can be selected. For example, if converted to hourly accumulation, you can use:
An insider threat detection system using machine learning and the ELK stack
Detecting insider threats in an organization by analyzing user computer data using LSTM autoencoder model
With r6.2
An intelligent classifier based Intrusion Detection System for IoVβs based on Neural Networks like optimized CNN, transfer learning and ensemble learning techniques and Hyper Parameter Optimizations.
The CERT Division, in partnership with ExactData, LLC, and under sponsorship from DARPA I2O, generated a collection of synthetic insider threat test datasets. These datasets provide both synthetic background data and data from synthetic malicious actors. For more background on this data, please see the paper, Bridging the Gap: A Pragmatic Approach to Generating Insider Threat Data. Datasets are organized according to the data generator release that created them. Most releases include multiple datasets (e.g., r3.1 and r3.2).
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network implemented in Keras
CNN based autoencoder combined with kernel density estimation for colour image anomaly detection / novelty detection. Built using Tensforflow 2.0 and Keras
Latest development of ISR/VSR. Papers and related resources, mainly state-of-the-art and novel works in ICCV, ECCV and CVPR about image super-resolution and video super-resolution.
Learning Tensorflow Step by Step:: Concepts, Examples & Applications
A collection of pre-trained, state-of-the-art models in the ONNX format
Models and examples built with TensorFlow
This repository contains various models targetting multimodal representation learning, multimodal fusion for downstream tasks such as multimodal sentiment analysis.
NUMODRIL - Nuclear Morphology Optimized Deep Hybrid Learning
Papers with code. Sorted by stars. Updated weekly.
Classical-Quantum hybrid model for credit card fraud detection
Quantum Hybrid Neural Network model for image classification
Recent advances in many fields have accelerated the demand for classification, regression, and detection problems from few 2D images/projections. Often, the heart of these modern techniques utilize neural networks, which can be implemented with deep learning algorithms. In our neural network architecture, we embed a dynamically programmable quantum circuit, acting as a hidden layer, to learn the correct parameters to correctly classify handwritten digits from the MNIST database. By starting small and making incremental improvements, we successfully reach a stunning ~95% accuracy on identifying previously unseen digits from 0 to 7 using this architecture!
Quantum Deep learning Binary Classification
This is an exploration using synthetic data in CSV format to apply QML models for the sake of binary classification. You can find here three different approaches. Two with Qiskit (VQC and QK/SVC) and one with Pennylane (QVC).
List of implementation of SOTA deep anomaly detection methods
HR to LR sample
An implementation of the Paper by Ledig, et al. (2017) titled 'Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network' using TensorFlow2 and Keras
State-of-the-art image super resolution models for PyTorch.
An Open Source Machine Learning Framework for Everyone
A declarative, efficient, and flexible JavaScript library for building user interfaces.
π Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. πππ
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google β€οΈ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.