Alternatives to DataMelt

TIMi allows companies to use their corporate data to generate new ideas and make crucial business decisions more quickly and easily than ever before. The heart of TIMi’s Integrated Platform. TIMi's ultimate real time AUTO-ML engine. 3D VR segmentation, visualization. Unlimited self service business Intelligence. TIMi is a faster solution than any other to perform the 2 most critical analytical tasks: data cleaning, feature engineering, creation KPIs, and predictive modeling. TIMi is an ethical solution. There is no lock-in, just excellence. We guarantee you work in complete serenity, without unexpected costs. TIMi's unique software infrastructure allows for maximum flexibility during the exploration phase, and high reliability during the production phase. TIMi allows your analysts to test even the most crazy ideas.

The Microsoft Cognitive Toolkit is an open-source toolkit that allows commercial-grade distributed deep-learning. It describes neural networks using a directed graph, which is a series of computational steps. CNTK makes it easy to combine popular models such as feed-forward DNNs (CNNs), convolutional neural network (CNNs), and recurrent neural network (RNNs/LSTMs) with ease. CNTK implements stochastic grade descent (SGD, error-backpropagation) learning with automatic differentiation/parallelization across multiple GPUs or servers. CNTK can be used in your Python, C# or C++ programs or as a standalone machine learning tool via its own model description language (BrainScript). You can also use the CNTK model assessment functionality in your Java programs. CNTK is compatible with 64-bit Linux and 64-bit Windows operating system. You have two options to install CNTK: you can choose pre-compiled binary packages or you can compile the toolkit using the source available in GitHub.

JMP, data analysis software Mac and Windows, combines powerful statistics with interactive visualization. It is simple to import and process data. Drag-and-drop interface, dynamically linked graphics, libraries of advanced analytics functionality, scripting language, and ways to share findings with others allow users to dig deeper into their data with greater ease. JMP was originally developed in 1980 to capture the new value of GUI for personal computers. JMP continues to add cutting-edge statistical methods to the software's functionality with every release. John Sall, the organization's founder, is still Chief Architect.

The hybrid front-end seamlessly switches between Gluon eager symbolic mode and Gluon imperative mode, providing flexibility and speed. The dual parameter server and Horovod support enable scaleable distributed training and performance optimization for research and production. Deep integration into Python, support for Scala and Julia, Clojure and Java, C++ and R. MXNet is supported by a wide range of tools and libraries that allow for use-cases in NLP, computer vision, time series, and other areas. Apache MXNet is an Apache Software Foundation (ASF) initiative currently incubating. It is sponsored by the Apache Incubator. All accepted projects must be incubated until further review determines that infrastructure, communications, decision-making, and decision-making processes have stabilized in a way consistent with other successful ASF projects. Join the MXNet scientific network to share, learn, and receive answers to your questions.

NLREG is a powerful statistical analysis software that performs surface and curve fitting, linear and nonlinear regression analysis and surface analysis. NLREG calculates the parameters of an equation whose form you specify that will cause the equation to be the most suitable for a given set of data values. NLREG can handle linear and nonlinear functions. NLREG is able to handle any function that you can algebraically specify, unlike many "nonlinear” regression programs. NLREG has a full programming language that uses a syntax similar C to specify the function to be fitted to data. This allows you to use conditionals and compute intermediate work variables. You can also iterate in loops. NLREG makes it easy to create piecewise functions that can change in form across different domains. The NLREG language supports arrays so you can use tabular lookup methods to define the function.

Altair Compose allows you to do math calculations, manipulate, and visualize data, program, and debug scripts that can be used for repeated computations or process automation. Compose lets users perform a variety of math operations, including matrix manipulations and linear algebra, as well as signal processing, control systems and polynomial fitting and optimization.

DL4J makes use of the most recent distributed computing frameworks, including Apache Spark and Hadoop, to accelerate training. It performs almost as well as Caffe on multi-GPUs. The libraries are open-source Apache 2.0 and maintained by Konduit and the developer community. Deeplearning4j is written entirely in Java and compatible with any JVM language like Scala, Clojure or Kotlin. The underlying computations are written using C, C++, or Cuda. Keras will be the Python API. Eclipse Deeplearning4j, a commercial-grade, open source, distributed deep-learning library, is available for Java and Scala. DL4J integrates with Apache Spark and Hadoop to bring AI to business environments. It can be used on distributed GPUs or CPUs. When training a deep-learning network, there are many parameters you need to adjust. We have tried to explain them so that Deeplearning4j can be used as a DIY tool by Java, Scala and Clojure programmers.

Deep learning frameworks like TensorFlow and PyTorch, Torch and Torch, Theano and MXNet have helped to increase the popularity of deep-learning by reducing the time and skills required to design, train and use deep learning models. Fabric for Deep Learning (pronounced "fiddle") is a consistent way of running these deep-learning frameworks on Kubernetes. FfDL uses microservices architecture to reduce the coupling between components. It isolates component failures and keeps each component as simple and stateless as possible. Each component can be developed, tested and deployed independently. FfDL leverages the power of Kubernetes to provide a resilient, scalable and fault-tolerant deep learning framework. The platform employs a distribution and orchestration layer to allow for learning from large amounts of data in a reasonable time across multiple compute nodes.

Neural Designer is a data-science and machine learning platform that allows you to build, train, deploy, and maintain neural network models. This tool was created to allow innovative companies and research centres to focus on their applications, not on programming algorithms or programming techniques. Neural Designer does not require you to code or create block diagrams. Instead, the interface guides users through a series of clearly defined steps. Machine Learning can be applied in different industries. These are some examples of machine learning solutions: - In engineering: Performance optimization, quality improvement and fault detection - In banking, insurance: churn prevention and customer targeting. - In healthcare: medical diagnosis, prognosis and activity recognition, microarray analysis and drug design. Neural Designer's strength is its ability to intuitively build predictive models and perform complex operations.

Open source machine learning and data visualization. With a wide range of tools, you can create data analysis workflows visually. Simple data analysis can be done with data visualization. Explore statistical distributions, box and scatter plots. Or dive deeper with decision trees and hierarchical clustering, heatmaps and MDS. Smart attribute ranking and selections can make multidimensional data more sensible in 2D. Interactive data exploration allows for qualitative analysis in a quick and efficient manner. The graphic user interface allows you focus on exploratory data analysis and not coding. Smart defaults make prototyping a data analysis workflow very easy. Connect widgets to the canvas, place them on the screen, and then load your data. We like to show data mining rather than just explain it. Orange excels at this.

lf you have data to analyze--but you're a researcher, not a statistician--Statistix is designed for you. In minutes, you can be up and running without programming or reading the manual. This software is easy to use and simple to learn, which will save you valuable time and money. Statistix offers all the advanced and basic statistics you need, as well as powerful data manipulation tools in one affordable package. Statistix provides powerful data manipulation tools, Excel import/export support, text files, linear models (including logistic regression, Poisson regression and ANOVA), nonlinear tests, time series and association tests, survival analysis and quality control, power analysis and many more.

The GPUs are fast at transferring data, but they have very limited locality of reference due to their small caches. They are designed to apply a lot compute to little data, and not a lot compute to a lot data. They are designed to run full layers of computation in order to fully fill their computational pipeline. (See Figure 1 below). Because large models have small memory sizes (tens to gigabytes), GPUs are placed together and models are distributed across them. This creates a complicated and painful software stack. It also requires synchronization and communication between multiple machines. The CPUs on the other side have much larger caches than GPUs and a lot of memory (terabytes). A typical CPU server may have memory equivalent to hundreds or even tens of GPUs. The CPU is ideal for a brain-like ML environment in which pieces of a large network are executed as needed.

Keras is an API that is designed for humans, not machines. Keras follows best practices to reduce cognitive load. It offers consistent and simple APIs, minimizes the number required for common use cases, provides clear and actionable error messages, as well as providing clear and actionable error messages. It also includes extensive documentation and developer guides. Keras is the most popular deep learning framework among top-5 Kaggle winning teams. Keras makes it easy to run experiments and allows you to test more ideas than your competitors, faster. This is how you win. Keras, built on top of TensorFlow2.0, is an industry-strength platform that can scale to large clusters (or entire TPU pods) of GPUs. It's possible and easy. TensorFlow's full deployment capabilities are available to you. Keras models can be exported to JavaScript to run in the browser or to TF Lite for embedded devices on iOS, Android and embedded devices. Keras models can also be served via a web API.

Google Cloud allows you to quickly build your deep learning project. You can quickly prototype your AI applications using Deep Learning Containers. These Docker images are compatible with popular frameworks, optimized for performance, and ready to be deployed. Deep Learning Containers create a consistent environment across Google Cloud Services, making it easy for you to scale in the cloud and shift from on-premises. You can deploy on Google Kubernetes Engine, AI Platform, Cloud Run and Compute Engine as well as Docker Swarm and Kubernetes Engine.

Mipsology's Zebra is the ideal Deep Learning compute platform for neural network inference. Zebra seamlessly replaces or supplements CPUs/GPUs, allowing any type of neural network to compute more quickly, with lower power consumption and at a lower price. Zebra deploys quickly, seamlessly, without any knowledge of the underlying hardware technology, use specific compilation tools, or modifications to the neural network training, framework, or application. Zebra computes neural network at world-class speeds, setting a new standard in performance. Zebra can run on the highest throughput boards, all the way down to the smallest boards. The scaling allows for the required throughput in data centers, at edge or in the cloud. Zebra can accelerate any neural network, even user-defined ones. Zebra can process the same CPU/GPU-based neural network with the exact same accuracy and without any changes.

NVIDIA DeepLearning GPU Training System (DIGITS), puts deep learning in the hands of data scientists and engineers. DIGITS is a fast and accurate way to train deep neural networks (DNNs), for image classification, segmentation, and object detection tasks. DIGITS makes it easy to manage data, train neural networks on multi-GPU platforms, monitor performance with advanced visualizations and select the best model from the results browser for deployment. DIGITS is interactive, so data scientists can concentrate on designing and training networks and not programming and debugging. TensorFlow allows you to interactively train models and TensorBoard lets you visualize the model architecture. Integrate custom plugs to import special data formats, such as DICOM, used in medical imaging.

Automaton AI's Automaton AI's DNN model and training data management tool, ADVIT, allows you to create, manage, and maintain high-quality models and training data in one place. Automated optimization of data and preparation for each stage of the computer vision pipeline. Automate data labeling and streamline data pipelines in house Automate the management of structured and unstructured video/image/text data and perform automated functions to refine your data before each step in the deep learning pipeline. You can train your own model with accurate data labeling and quality assurance. DNN training requires hyperparameter tuning such as batch size, learning rate, and so on. To improve accuracy, optimize and transfer the learning from trained models. After training, the model can be put into production. ADVIT also does model versioning. Run-time can track model development and accuracy parameters. A pre-trained DNN model can be used to increase the accuracy of your model for auto-labeling.

The study of numerical analysis or Scientific computing, also known as numerical analysis, is the study and application of approximation techniques to numerically solve mathematical problems. Scilab offers graphics functions that allow you to visualize, annotate, and export data. It also allows you to customize many types of charts and plots. Scilab is a high-level programming language for scientific programming. It allows rapid prototyping of algorithms without the need to deal with the complexity and limitations of C or Fortran (memory management, variables definition). Scilab is able to handle complex mathematical operations. Other languages would require more code. It has advanced data structures such as polynomials and matrices, and provides an easy-to-use development environment.

ndCurveMaster, a specialized curve fitting software, is designed to fit curves with multiple variables. It automatically applies nonlinear equations to your datasets. These can be observed or measured values. The software supports curve and surfaces fitting in 2D 3D 4D 5D ..., dimensions. ndCurveMaster is able to handle any data, no matter how complex or how many variables there are. ndCurveMaster, for example, can efficiently derive the optimal equations for a dataset that has six inputs (x1-x6) and a corresponding output Y. For example: Y = a0 - a1 - exp(x1)0.5 + a2 ln(x2)8... + a6 x65.2 to accurately match measured value. ndCurveMaster uses machine learning numerical methods to automatically fit the most suitable nonlinear regression function to your dataset, and discover the relationships between inputs and outputs. This tool supports various curve fitting methods, including linear, polynomial, and nonlinear methods. It also utilizes essential validation and goodness-of-fit tests to ensure accuracy. Additionally, ndCurveMaster provides advanced assessments, such as detecting overfitting and multicollinearity, using tools like the Variance Inflation Factor (VIF) and the Pearson correlation matrix.

NVIDIA Modulus, a neural network framework, combines the power of Physics in the form of governing partial differential equations (PDEs), with data to create high-fidelity surrogate models with near real-time latency. NVIDIA Modulus is a tool that can help you solve complex, nonlinear, multiphysics problems using AI. This tool provides the foundation for building physics machine learning surrogate models that combine physics and data. This framework can be applied to many domains and uses, including engineering simulations and life sciences. It can also be used to solve forward and inverse/data assimilation issues. Parameterized system representation that solves multiple scenarios in near real-time, allowing you to train once offline and infer in real-time repeatedly.

SHARK is an open-source C++ machine-learning library that is fast, modular, and feature-rich. It offers methods for linear and unlinear optimization, kernel-based algorithms, neural networks, as well as other machine learning techniques. It is a powerful toolbox that can be used in real-world applications and research. Shark relies on Boost, CMake. It is compatible with Windows and Solaris, MacOS X and Linux. Shark is licensed under the permissive GNU Lesser General Public License. Shark offers a great compromise between flexibility and ease of use and computational efficiency. Shark provides many algorithms from different domains of machine learning and computational intelligence that can be combined and extended easily. Shark contains many powerful algorithms that, to our best knowledge, are not available in any other library.

KEEL (Knowledge Extraction Based on Evolutionary Learning), is a Java open-source (GPLv3) Java software that can be used to perform a variety of knowledge data discovery tasks. KEEL is a simple GUI that uses data flow to design experiments using different datasets and computational algorithms (paying particular attention to evolutionary algorithms). This allows you to evaluate the behavior of the algorithms. It includes a variety of knowledge extraction algorithms, preprocessing methods (training set selection, feature choice, discretization, and imputation methods to missing values among others), computational intelligence-based learning algorithms, hybrid models, statistical methods for contrasting experiments, and so forth. It allows you to compare new proposals for computational intelligence with existing ones. KEEL was designed with two goals: education and research.

MATLAB®, a combination of a desktop environment for iterative analysis, design processes, and a programming language that expresses matrix or array mathematics directly, is MATLAB®. It also includes the Live Editor, which allows you to create scripts that combine output, code, and formatted text in an executable notebook. MATLAB toolboxes have been professionally developed, tested and documented. MATLAB apps allow you to see how different algorithms interact with your data. You can repeat the process until you get the results you desire. Then, MATLAB will automatically generate a program to replicate or automate your work. With minor code changes, you can scale your analyses to run on GPUs, clusters, and clouds. You don't need to rewrite any code or learn big-data programming and other out-of-memory methods. Convert MATLAB algorithms automatically to C/C++ and HDL to run on your embedded processor/FPGA/ASIC. Simulink works with MATLAB to support Model-Based Design.

DeepCube is a company that focuses on deep learning technologies. This technology can be used to improve the deployment of AI systems in real-world situations. The company's many patent innovations include faster, more accurate training of deep-learning models and significantly improved inference performance. DeepCube's proprietary framework is compatible with any hardware, datacenters or edge devices. This allows for over 10x speed improvements and memory reductions. DeepCube is the only technology that allows for efficient deployment of deep-learning models on intelligent edge devices. The model is typically very complex and requires a lot of memory. Deep learning deployments today are restricted to the cloud because of the large amount of memory and processing requirements.

Torch is a scientific computing platform that supports machine learning algorithms and has wide support for them. It is simple to use and efficient thanks to a fast scripting language, LuaJIT and an underlying C/CUDA implementation. Torch's goal is to allow you maximum flexibility and speed when building your scientific algorithms, while keeping it simple. Torch includes a large number of community-driven packages for machine learning, signal processing and parallel processing. It also builds on the Lua community. The core of Torch is the popular optimization and neural network libraries. These libraries are easy to use while allowing for maximum flexibility when implementing complex neural networks topologies. You can create arbitrary graphs of neuro networks and parallelize them over CPUs or GPUs in an efficient way.

The NVIDIA GPU Optimized AMI is a virtual image that accelerates your GPU-accelerated Machine Learning and Deep Learning workloads. This AMI allows you to spin up a GPU accelerated EC2 VM in minutes, with a preinstalled Ubuntu OS and GPU driver. Docker, NVIDIA container toolkit, and Docker are also included. This AMI provides access to NVIDIA’s NGC Catalog. It is a hub of GPU-optimized software for pulling and running performance-tuned docker containers that have been tested and certified by NVIDIA. The NGC Catalog provides free access to containerized AI and HPC applications. It also includes pre-trained AI models, AI SDKs, and other resources. This GPU-optimized AMI comes free, but you can purchase enterprise support through NVIDIA Enterprise. Scroll down to the 'Support information' section to find out how to get support for AMI.

Deci's deep learning platform powered by Neural architecture Search allows you to quickly build, optimize, deploy, and deploy accurate models. You can instantly achieve accuracy and runtime performance that is superior to SoTA models in any use case or inference hardware. Automated tools make it easier to reach production. No more endless iterations or dozens of libraries. Allow new use cases for resource-constrained devices and cut down on your cloud computing costs by up to 80% Deci's NAS-based AutoNAC engine automatically finds the most appropriate architectures for your application, hardware, and performance goals. Automately compile and quantify your models using the best of breed compilers. Also, quickly evaluate different production settings.

We conduct cutting-edge research in artificial intelligence and implement it to give financial investors an advantage. Transforming the financial market through groundbreaking neuro-prediction. Our algorithms combine graph-based learning and deep reinforcement learning algorithms to model and predict time series. Neuri aims to generate synthetic data that mimics the global financial markets and test it with complex simulations. Quantum optimization is the future of supercomputing. Our simulations will be able to exceed the limits of classical supercomputing. Financial markets are dynamic and change over time. We develop AI algorithms that learn and adapt continuously to discover the connections between different financial assets, classes, and markets. The application of neuroscience-inspired models, quantum algorithms and machine learning to systematic trading at this point is underexplored.

NaturalText A.I. Your data can be used to get more. Discover relationships, build collections, and uncover hidden insights in documents and text-based data. NaturalText A.I. NaturalText A.I. uses artificial intelligence technology to uncover hidden data relationships. The software uses a variety of state-of-the art methods to understand context and analyze patterns to reveal insights - all in a human-readable manner. Discover hidden insights in your data It can be difficult, if not impossible, to find everything in your text data. Traditional search can only find information about a document. NaturalText A.I. on the other hand, uncovers new data within millions of documents, including patents and scientific papers. NaturalText A.I. NaturalText A.I. can help you uncover insights in your data that you are not currently seeing.

CoPlot Version 6.45 can be used to create publication-quality 2D or 3D scientific graphs. These graphs plot data and equations, maps, and technical drawings. CoPlot was designed with one goal in mind: to make a program so flexible that engineers and scientists can get exactly what they need. CoPlot also includes CoStat, which is used to handle data and provide statistics. CoPlot's flexible drawing objects allow you to create precise technical drawings. CoPlot is great for creating field maps, genetic maps, flow charts and chemical structures. Check out the samples. Text in graphs and drawing objects can include HTML-like text formatting tags, and over 1000 special characters. Create amazing scientific graphs and maps. CoPlot offers seven types of graphs, more than 40 ways to plot data, 18 ways for equations to be plotted, flexible attributes, asymmetrical and horizontal error bars, 12 axis types, and many other features.

RapidMiner is redefining enterprise AI so anyone can positively shape the future. RapidMiner empowers data-loving people from all levels to quickly create and implement AI solutions that drive immediate business impact. Our platform unites data prep, machine-learning, and model operations. This provides a user experience that is both rich in data science and simplified for all others. Customers are guaranteed success with our Center of Excellence methodology, RapidMiner Academy and no matter what level of experience or resources they have.

Alteryx is the launchpad to automation breakthroughs. The results are unrivalled, whether you're looking for personal growth, rapid innovation, or transformative digital outcomes. This unique innovation combines analytics, data science, and process automation into a single platform that empowers every person and organization to make business-changing breakthroughs.

EntelliFusion by Teksouth is a fully managed, end to end solution. EntelliFusion's architecture is a one-stop solution for outfitting a company's data infrastructure. Instead of trying to put together multiple platforms for data prep, data warehouse and governance, and then deploying a lot of IT resources to make it all work, EntelliFusion's architecture offers a single platform. EntelliFusion unites data silos into a single platform that allows for cross-functional KPI's. This creates powerful insights and holistic solutions. EntelliFusion's "military born" technology has been able to withstand the rigorous demands of the USA's top echelon in military operations. It was scaled up across the DOD over twenty years. EntelliFusion is built using the most recent Microsoft technologies and frameworks, which allows it to continue being improved and innovated. EntelliFusion is data-agnostic and infinitely scalable. It guarantees accuracy and performance to encourage end-user tool adoption.

This econometric modeling program features an intuitive interface and one the most comprehensive data management tools. It allows you to quickly and efficiently create forecasting and statistical equations. You will enjoy the best-in class features such as 64-bit Windows large-memory support, object linking, embedding (OLE), and smart edit windows. Analyze time series, longitudinal and cross-section data quickly. Streamline statistical modeling and econometric analysis. Produce presentations-quality tables and graphs. Superior budgeting, strategic planning, and academic research are all possible. Menus that are context-sensitive. Batch programming language. Tools for adding-ins and user objects. Full command-line support. Drag-and-drop functionality. You can generate forecasts and model simulations. High-quality tables and graphs can be published or used in other applications. EViews 12 provides more power and ease-of use than you have come to expect.

Origin is the preferred data analysis and graphing tool for more than half a million engineers and scientists in government and commercial laboratories around the world. Origin has an intuitive interface that is easy to use for beginners. You can also customize the program as you get more familiar. Origin graphs and analysis results can automatically adjust to data changes or parameter changes. This allows you to create templates or perform batch operations directly from the user interface without programming. Install free Apps from our website to extend the capabilities of Origin. Connect to other applications like MATLAB™, LabVIEW™, or Microsoft(c) Excel. Create custom routines in Origin with our scripting and C languages. OriginPro takes data analysis to the next level. OriginPro provides advanced analysis tools in addition to all the features of Origin.

Stata is a comprehensive, integrated software package that can handle all aspects of data science: data manipulation, visualization and statistics, as well as automated reporting. Stata is quick and accurate. The extensive graphical interface makes it easy to use, but is also fully programable. Stata's menus, dialogs and buttons give you the best of both worlds. All Stata's data management, statistical, and graphical features are easy to access by dragging and dropping or point-and-click. To quickly execute commands, you can use Stata's intuitive command syntax. You can log all actions and results, regardless of whether you use the menus or dialogs. This will ensure reproducibility and integrity in your analysis. Stata also offers complete command-line programming and programming capabilities, including a full matrix language. All the commands that Stata ships with are available to you, whether you want to create new Stata commands or script your analysis.

Built-in industry-standard models with support for creating and sharing your own models. XLfit®, a Microsoft®, Excel add-in for Windows, brings scientific mathematics and statistics to Excel. It also supports charting capabilities. XLfit, the most popular curve fitting and statistical package for Excel, is used by some of the world's top pharmaceutical, chemical, engineering, and research institutions. It is also validated by National Physical Laboratory (NPL). XLfit has over 70 models that can be used to perform nonlinear and linear curve fittings. This includes all the models commonly used for describing data from drug-discovery-related experiments. You can add unlimited numbers of custom or user-defined model. Linear and nonlinear modeling, interactive 3D and 2-D chartsing, point knockout, and automatic and interactive point knockout are all possible. Every scientist needs these features.

Combine Data Science, Business Intelligence and Data Management Capabilities into One Integrated, Self-Serve Platform. Analance is an end-to-end platform with robust and salable features that combines Data Science and Advanced Analytics, Business Intelligence and Data Management into a single integrated platform. It provides core analytical processing power to ensure that data insights are easily accessible to all, performance remains consistent over time, and business objectives can be met within a single platform. Analance focuses on making quality data into accurate predictions. It provides both citizen data scientists and data scientists with pre-built algorithms as well as an environment for custom programming. Company - Overview Ducen IT provides advanced analytics, business intelligence, and data management to Fortune 1000 companies through its unique data science platform Analance.

Integrate COMSOL Multiphysics®, with MATLAB®, to extend your modeling and scripting programming within the MATLAB environment. LiveLink™, for MATLAB®, allows you to harness the power of MATLAB's toolboxes and preprocessing, modeling manipulation, and postprocessing. Multiphysics simulations can be used to enhance your MATLAB code. Your geometry modeling should be based on probabilistic or image data. Multiphysics models can be combined with Monte Carlo simulations or genetic algorithms. To incorporate COMSOL models into control systems, export COMSOL models in state-space matrix format. The COMSOL Desktop®, interface allows you to use MATLAB®, functions while modeling. To parameterize the geometry, physics or solution scheme, you can manipulate your models using the command line or script.

This is the latest version of MCM Alchimia's freeware application. It was specifically designed to estimate uncertainty in measurement and calibrations using the Monte Carlo method, consistent with JCGM 101. This version adds a complete GUM framework uncertainty buget, and like the previous version, supports correlated quantities as well as regression curves. Speed improvements comparable to popular statistics and calculation software. Simulation in direct, indirect and total least squares. External module allows for custom application language. Output report with extensive statistical analysis of the simulation.

Mintrics is the ultimate social media analytics dashboard with market and competitor intelligence. It allows brands, agencies, content creators, and marketers to see which videos are performing well and which aren’t and why. Mintrics allows you to analyze all your videos on YouTube and Facebook in one place. It connects to various APIs using users' tokens to collect data that isn't available publicly. It runs all calculations and displays unique metrics with historical information. Mintrics provides benchmarks, monthly reports and personalized recommendations, as metrics can be useless by themselves. First, at a page/channel-level to clearly show how a video is performing against others. Then, industry benchmarks that show performance compared to the competition. Mintrics Live Leaderboard allows you to track and group your competitors, as well as view market insights.

ChemStat is the fastest and easiest way to analyze ground water monitoring data at RCRA facilities. ChemStat supports most of the statistical analysis methods described by the USEPA statistical analysis documents from 1989 and 1992, USEPA Draft Unified Guidance document, U.S. Navy Statistical Analysis Guidance, and other guidance documents. ChemStat is the leader in environmental statistical analysis due to its unique combination of innovative technologies and ease-of-use. The most common tests have a limited data set size. Unlimitless number of parameters. Unlimited number of wells. Unlimited number of sample dates. Unlimited length of well labels and parameter names. You can easily exclude data points from your analyses.

NeuroIntelligence, a software application for neural networks, is designed to help experts in data mining, predictive modeling, pattern recognition, and neural network design in solving real-world problems. NeuroIntelligence uses only proven neural net modeling algorithms and techniques. It is easy to use and fast. Visualized architecture search, training and testing of neural networks. Neural network architecture search. Fitness bars. Network training graphs comparison. Training graphs, dataset error and network error, weights distribution, neural network input importance, and errors distribution Testing, actual vs. output graph, scatter plot and response graph, ROC curve and confusion matrix. NeuroIntelligence's interface is optimized to solve data mining and forecasting, classification, and pattern recognition problems. The tool's intuitive GUI and time-saving features make it easy to create a better solution faster.

Over the past 20 years, many organizations have developed SAS-language programs that are essential to their operations. Altair SLC can run programs written in SAS syntax without translation or needing third-party licenses. Altair SLC's ability to handle high throughput reduces capital costs and operating expenditures for users. Altair SLC has a built-in SAS compiler that runs SAS and SQL code. It also uses Python and R compilers for Python and R code. It can also exchange SAS datasets, Pandas and R data frames. The software runs on IBM mainframes and in the cloud as well as on servers and workstations that run a variety operating systems. It supports remote job submission as well as the ability to exchange information between mainframe, cloud and on-premises installations.

The Salford Predictive Modeler® (SPM), software suite, is highly accurate and extremely fast for developing predictive, descriptive, or analytical models. Salford Predictive Modeler®, which includes the CART®, TreeNet®, Random Forests® engines, and powerful new automation capabilities and modeling capabilities that are not available elsewhere, is a software suite that includes the MARS®, CART®, TreeNet[r], and TreeNet®. The SPM software suite's data mining technologies span classification, regression, survival analysis, missing value analysis, data binning and clustering/segmentation. SPM algorithms are essential in advanced data science circles. Automation of model building is made easier by the SPM software suite. It automates significant portions of the model exploration, refinement, and refinement process for analysts. We combine all results from different modeling strategies into one package for easy review.

The QMSys GUM Software can be used to analyze the uncertainty of physical measurements, chemical analysis and calibrations. The software uses three methods to calculate measurement uncertainty. GUF Method for linear model, this method can be applied to both linear and quasi-linear models. It corresponds to GUM Uncertainty Framework. The software calculates partial derivatives (the first term in a Taylor series) to determine sensitivity coefficients for the equivalent linear model. It then calculates the combined standard uncertainties according to the Gaussian error propagation laws. GUF Method for Nonlinear Models, this method is for models that have a symmetric distribution of the results quantities. A variety of numerical methods are used in this method, including: Nonlinear sensitivity analysis, second- and third-order sensitive indices, quasi Monte Carlo with Sobol sequences.

Statistics programs are often designed by statisticians for statisticians. These programs are feature-rich and powerful, but they can overwhelm scientists with dense manuals, obscure statistical terminology, and high prices. GraphPad InStat offers a different approach. InStat was designed by scientists for scientists. InStat makes it easy for even the most novice statistician to analyze data in a matter of minutes. InStat is a simple way to learn statistics. InStat takes you through statistical analyses and helps you to conquer the learning curve. In just a few minutes, you'll be able to master the program. You don't need to know the name or number of the test you need. InStat will help you choose the right test by asking you questions about your data. The extensive help screens will provide clear explanations of statistical reasoning in plain English. InStat doesn't assume you're a statistician. It presents results in plain English, with little statistical jargon. The help screens in InStat explain how to use each test.

Darknet is an open-source framework for neural networks written in C and CUDA. It is easy to install and supports both CPU and GPU computation. The source code can be found on GitHub. You can also read more about Darknet's capabilities. Darknet is easy-to-install with only two dependencies: OpenCV if your preference is for a wider range of image types and CUDA if your preference is for GPU computation. Darknet is fast on the CPU, but it's about 500 times faster on the GPU. You will need an Nvidia GPU, and you'll need to install CUDA. Darknet defaults to using stb_image.h to load images. OpenCV is a better alternative to Darknet. It supports more formats, such as CMYK jpegs. Thanks to Obama! OpenCV allows you to view images, and detects without saving them to disk. You can classify images using popular models such as ResNet and ResNeXt. For NLP and time-series data, recurrent neural networks are a hot trend.

We make all your data actionable. AutoAlert's behavior-predicting platform helps customers identify those who can upgrade. AlertMiner combines thousands of customer metrics into one snapshot to provide a clear path to engagement. Predictive analytics score identifies your top opportunities. The easy-to-read alerts will tell you why. Dynamic phone scripts make it easy to make phone calls. AutoAlert is the first automotive industry CXM (Customer experience Management) Platform. It is also "Building for the Future", as a leader in automotive intelligence software. AlertMiner shows you which customers are most likely to buy today and at what profit to car dealerships. AutoAlert is easy to use for every department. It unites teams around data to increase sales, service opportunities, and provide seamless customer experiences. AutoAlert is a leader in the field of data-mining software solutions that strengthen customer loyalty and build customer relationships.

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