29 skills found
M2Team / NanaBoxThe third-party lightweight XAML-based out-of-box-experience oriented Hyper-V virtualization software based on Host Compute System API, Remote Desktop ActiveX control and XAML Islands
awslabs / Quantum Computing Exploration For Drug Discovery On AwsDeploy a solution to research on drug discovery problems using quantum computing and classical computing resources.
blindedjoy / RcTorcha PyTorch based Reservoir Computing package with Automatic Hyper-Parameter Tuning
BGU-CS-VIL / DPMMSubClusters.jlDistributed MCMC Inference in Dirichlet Process Mixture Models (High Performance Machine Learning Workshop 2019)
gokadin / Hyperdimensional ComputingHyperdimensional computing explained and demonstrated
Zheng-Meng / Reservoir Computing And Hyperparameter OptimizationReservoir computing for short-and long-term prediction of chaotic systems, with tasks Lorenz and Mackey-Glass systems. Bayesian optimization (hyperparameter optimization algorithm) is used to tune the hyperparameters and improve the performance.
Aghoreshwar / Awesome Customer AnalyticsCustomer analytics has been one of hottest buzzwords for years. Few years back it was only marketing department’s monopoly carried out with limited volumes of customer data, which was stored in relational databases like Oracle or appliances like Teradata and Netezza. SAS & SPSS were the leaders in providing customer analytics but it was restricted to conducting segmentation of customers who are likely to buy your products or services. In the 90’s came web analytics, it was more popular for page hits, time on sessions, use of cookies for visitors and then using that for customer analytics. By the late 2000s, Facebook, Twitter and all the other socialchannels changed the way people interacted with brands and each other. Businesses needed to have a presence on the major social sites to stay relevant. With the digital age things have changed drastically. Customer issuperman now. Their mobile interactions have increased substantially and they leave digital footprint everywhere they go. They are more informed, more connected, always on and looking for exceptionally simple and easy experience. This tsunami of data has changed the customer analytics forever. Today customer analytics is not only restricted to marketing forchurn and retention but more focus is going on how to improve thecustomer experience and is done by every department of the organization. A lot of companies had problems integrating large bulk of customer data between various databases and warehouse systems. They are not completely sure of which key metrics to use for profiling customers. Hence creating customer 360 degree view became the foundation for customer analytics. It can capture all customer interactions which can be used for further analytics. From the technology perspective, the biggest change is the introduction of big data platforms which can do the analytics very fast on all the data organization has, instead of sampling and segmentation. Then came Cloud based platforms, which can scale up and down as per the need of analysis, so companies didn’t have to invest upfront on infrastructure. Predictive models of customer churn, Retention, Cross-Sell do exist today as well, but they run against more data than ever before. Even analytics has further evolved from descriptive to predictive to prescriptive. Only showing what will happen next is not helping anymore but what actions you need to take is becoming more critical. There are various ways customer analytics is carried out: Acquiring all the customer data Understanding the customer journey Applying big data concepts to customer relationships Finding high propensity prospects Upselling by identifying related products and interests Generating customer loyalty by discovering response patterns Predicting customer lifetime value (CLV) Identifying dissatisfied customers & churn patterns Applying predictive analytics Implementing continuous improvement Hyper-personalization is the center stage now which gives your customer the right message, on the right platform, using the right channel, at the right time. Now via Cognitive computing and Artificial Intelligence using IBM Watson, Microsoft and Google cognitive services, customer analytics will become sharper as their deep learning neural network algorithms provide a game changing aspect. Tomorrow there may not be just plain simple customer sentiment analytics based on feedback or surveys or social media, but with help of cognitive it may be what customer’s facial expressions show in real time. There’s no doubt that customer analytics is absolutely essential for brand survival.
overcat / FastcrcA hyper-fast Python module for computing CRC(8, 16, 32, 64) checksum.
Cornell-RelaxML / Hyperdimensional ComputingOfficial implementation for the paper "Understanding Hyperdimensional Computing for Parallel Single-Pass Learning"
RHsyseng / HciHyper Converged Infrastructure: Scripts and Templates to deploy OpenStack with Nova Computes and Ceph-OSDs running on the same server using OSPd for Red Hat OpenStack Platform 10
openstack-archive / Compute HypervRETIRED, Nova Hyper-V Compute Driver. Mirror of code maintained at opendev.org.
RichardAragon / HyperDimensionalComputingNeuralNetworkNo description available
lasy / Alto'alto' is an R package that aligns topics from different LDA models, computes metrics for quantifying the goodness of alignment, and provides visualization functions to explore the alignment and robustness of topics across environments or LDA hyper-parameters.
Kermit-UGent / HyperdimensionalComputing.jlHyperdimensional computing in Julia
ananace / Foreman HypervMicrosoft Hyper-V compute resource for Foreman
WesleyHsieh0806 / HDCWes's ML Project ---HDC(Hyper Dimensional Computing)
stblake / Algebraic IntegrationA suite of algorithms for computing algebraic (pseudo- and hyper-elliptic, nested radicals) integrals in terms of elementary functions.
denkle / Binary Hyperdimensional Computing Trade Offs In Choice Of Density And MappingNo description available
ppouliot / Puppet Openstack Hyper VA starting point for building Windows/Hyper-V OpenStack Compute nodes
InKnowWorks / RDF Graph And HypergraphRDF Data Graphs: Property and Hyper Data Modeling using Microsoft GraphEngine TSL, Computed DSL, LIKQ and APIs
