Best Deep Learning Software for Linux

Fraud.net delivers the world’s most advanced infrastructure for fraud management – powered by a sophisticated collective intelligence network, world-class artificial intelligence, and a modern, cloud-based platform that helps you: * Unify fraud data from any source with a single connection * Detect fraudulent activity for 99.5%+ transactions in real-time * Optimize fraud management by uncovering hidden insights in terabytes of data Recognized in Gartner’s Market Guide for Online Fraud Detection, Fraud.net is a real-time, enterprise-strength fraud prevention and analytics solution organized around its business customers’ needs. Through a single point of command, it unifies and analyzes data from disparate systems and sources, tracks digital identities and behaviors, and then deploys the latest tools and technologies to stamp out fraudulent activity while allowing good transactions to sail through. Contact us today for a free trial.

DataMelt (or "DMelt") is an environment for numeric computation, data analysis, data mining, computational statistics, and data visualization. DataMelt can be used to plot functions and data in 2D and 3D, perform statistical tests, data mining, numeric computations, function minimization, linear algebra, solving systems of linear and differential equations. Linear, non-linear and symbolic regression are also available. Neural networks and various data-manipulation methods are integrated using Java API. Elements of symbolic computations using Octave/Matlab scripting are supported. DataMelt is a computational environment for Java platform. It can be used with different programming languages on different operating systems. Unlike other statistical programs, it is not limited to a single programming language. This software combines the world's most-popular enterprise language, Java, with the most popular scripting language used in data science, such as Jython (Python), Groovy, JRuby.

Neural Designer is a powerful software tool for developing and deploying machine learning models. It provides a user-friendly interface that allows users to build, train, and evaluate neural networks without requiring extensive programming knowledge. With a wide range of features and algorithms, Neural Designer simplifies the entire machine learning workflow, from data preprocessing to model optimization. In addition, it supports various data types, including numerical, categorical, and text, making it versatile for domains. Additionally, Neural Designer offers automatic model selection and hyperparameter optimization, enabling users to find the best model for their data with minimal effort. Finally, its intuitive visualizations and comprehensive reports facilitate interpreting and understanding the model's performance.

Keras is an API designed for human beings, not machines. Keras follows best practices for reducing cognitive load: it offers consistent & simple APIs, it minimizes the number of user actions required for common use cases, and it provides clear & actionable error messages. It also has extensive documentation and developer guides. Keras is the most used deep learning framework among top-5 winning teams on Kaggle. Because Keras makes it easier to run new experiments, it empowers you to try more ideas than your competition, faster. And this is how you win. Built on top of TensorFlow 2.0, Keras is an industry-strength framework that can scale to large clusters of GPUs or an entire TPU pod. It's not only possible; it's easy. Take advantage of the full deployment capabilities of the TensorFlow platform. You can export Keras models to JavaScript to run directly in the browser, to TF Lite to run on iOS, Android, and embedded devices. It's also easy to serve Keras models as via a web API.

NaturalText A.I. helps you get more out of your data. Discover relationships, create collections, and unveil hidden insights in documents and other text-based data. NaturalText A.I. uses novel artificial intelligence technology to uncover hidden relationships in data. The software uses various state-of-the-art methods to understand context, analyze patterns, and reveal insights—all in a human-readable way. Reveal insights hidden in your data. Finding everything hidden in your text data is a difficult, if not impossible, task. With traditional search, you can only locate information related to a document. NaturalText A.I., on the other hand, uncovers new information within millions of documents, including scientific papers and patents. Use NaturalText A.I. to reveal insights in the data you are currently missing.

ClearML is the leading open source MLOps and AI platform that helps data science, ML engineering, and DevOps teams easily develop, orchestrate, and automate ML workflows at scale. Our frictionless, unified, end-to-end MLOps suite enables users and customers to focus on developing their ML code and automation. ClearML is used by more than 1,300 enterprise customers to develop a highly repeatable process for their end-to-end AI model lifecycle, from product feature exploration to model deployment and monitoring in production. Use all of our modules for a complete ecosystem or plug in and play with the tools you have. ClearML is trusted by more than 150,000 forward-thinking Data Scientists, Data Engineers, ML Engineers, DevOps, Product Managers and business unit decision makers at leading Fortune 500 companies, enterprises, academia, and innovative start-ups worldwide within industries such as gaming, biotech , defense, healthcare, CPG, retail, financial services, among others.

Develop on your laptop and then scale the same Python code elastically across hundreds of nodes or GPUs on any cloud, with no changes. Ray translates existing Python concepts to the distributed setting, allowing any serial application to be easily parallelized with minimal code changes. Easily scale compute-heavy machine learning workloads like deep learning, model serving, and hyperparameter tuning with a strong ecosystem of distributed libraries. Scale existing workloads (for eg. Pytorch) on Ray with minimal effort by tapping into integrations. Native Ray libraries, such as Ray Tune and Ray Serve, lower the effort to scale the most compute-intensive machine learning workloads, such as hyperparameter tuning, training deep learning models, and reinforcement learning. For example, get started with distributed hyperparameter tuning in just 10 lines of code. Creating distributed apps is hard. Ray handles all aspects of distributed execution.

The Intel® Distribution of OpenVINO™ toolkit is an open-source AI development toolkit that accelerates inference across Intel hardware platforms. Designed to streamline AI workflows, it allows developers to deploy optimized deep learning models for computer vision, generative AI, and large language models (LLMs). With built-in tools for model optimization, the platform ensures high throughput and lower latency, reducing model footprint without compromising accuracy. OpenVINO™ is perfect for developers looking to deploy AI across a range of environments, from edge devices to cloud servers, ensuring scalability and performance across Intel architectures.

OTO allows call centers 100% visibility of what is said during customer calls within 20 hours. Complement your NPS scoring with in-call intonation analytics. Identify call agent engagement and proactively set your WFM plan. Pick calls for QA faster. OTO is language-agnostic and gives you output parameters on various angles. Our API allows companies to start analyzing 100% of in-call conversations within a couple of hours. Sign up for a free trial and start analyzing your call data! Voice is the most valuable touchpoint between you and your customer. We're here to help you truly understand and leverage your voice data at scale. Whether you're building a mobile app or data analytics dashboards, our lightweight DeepToneTM engine gives you access to our powerful voice models on any device, providing you with a rich layer of acoustic labels for nearly every audio format.

DeepSpeed is an open source deep learning optimization library for PyTorch. It's designed to reduce computing power and memory use, and to train large distributed models with better parallelism on existing computer hardware. DeepSpeed is optimized for low latency, high throughput training. DeepSpeed can train DL models with over a hundred billion parameters on the current generation of GPU clusters. It can also train up to 13 billion parameters in a single GPU. DeepSpeed is developed by Microsoft and aims to offer distributed training for large-scale models. It's built on top of PyTorch, which specializes in data parallelism.

We make it easy to add superhuman computer vision to your applications, devices and processes to give you unassailable competitive advantage. No code, customizable & on-premise AI solutions.

The NVIDIA GPU-Optimized AMI is a virtual machine image for accelerating your GPU accelerated Machine Learning, Deep Learning, Data Science and HPC workloads. Using this AMI, you can spin up a GPU-accelerated EC2 VM instance in minutes with a pre-installed Ubuntu OS, GPU driver, Docker and NVIDIA container toolkit. This AMI provides easy access to NVIDIA's NGC Catalog, a hub for GPU-optimized software, for pulling & running performance-tuned, tested, and NVIDIA certified docker containers. The NGC catalog provides free access to containerized AI, Data Science, and HPC applications, pre-trained models, AI SDKs and other resources to enable data scientists, developers, and researchers to focus on building and deploying solutions. This GPU-optimized AMI is free with an option to purchase enterprise support offered through NVIDIA AI Enterprise. For how to get support for this AMI, scroll down to 'Support Information'

Dragonfly 3D World by Object Research Systems (ORS) is a comprehensive software platform for multidimensional image visualization, analysis, and collaboration in scientific and industrial fields. It provides powerful tools to visualize, process, and interpret 2D, 3D, and 4D imaging data acquired through modalities such as CT, MRI, electron microscopy, and more. Dragonfly supports real-time volume rendering, surface rendering, and orthogonal slicing, allowing users to explore complex structures interactively. With its AI integration, users can apply deep learning for image segmentation, classification, and object detection. It offers advanced quantitative analysis tools, including region-of-interest analysis, measurements, and statistical evaluations. Its intuitive graphical user interface enables researchers to build reproducible workflows and perform batch processing.

The Intel® Deep Learning SDK is a set of tools for data scientists and software developers to develop, train, and deploy deep learning solutions. The SDK encompasses a training tool and a deployment tool that can be used separately or together in a complete deep learning workflow. Easily prepare training data, design models, and train models with automated experiments and advanced visualizations. Simplify the installation and usage of popular deep learning frameworks optimized for Intel® platforms. Easily prepare training data, design models, and train models with automated experiments and advanced visualizations. Simplify the installation and usage of popular deep learning frameworks optimized for Intel® platforms. The web user interface includes an easy to use wizard to create deep learning models, with tooltips to guide you through the entire process.

The VLFeat open source library implements popular computer vision algorithms specializing in image understanding and local features extraction and matching. Algorithms include Fisher Vector, VLAD, SIFT, MSER, k-means, hierarchical k-means, agglomerative information bottleneck, SLIC superpixels, quick shift superpixels, large scale SVM training, and many others. It is written in C for efficiency and compatibility, with interfaces in MATLAB for ease of use, and detailed documentation throughout. It supports Windows, Mac OS X, and Linux. MatConvNet is a MATLAB toolbox implementing Convolutional Neural Networks (CNNs) for computer vision applications. It is simple, efficient, and can run and learn state-of-the-art CNNs. Many pre-trained CNNs for image classification, segmentation, face recognition, and text detection are available.

TFlearn is a modular and transparent deep learning library built on top of Tensorflow. It was designed to provide a higher-level API to TensorFlow in order to facilitate and speed up experimentations while remaining fully transparent and compatible with it. Easy-to-use and understand high-level API for implementing deep neural networks, with tutorial and examples. Fast prototyping through highly modular built-in neural network layers, regularizers, optimizers, metrics. Full transparency over Tensorflow. All functions are built over tensors and can be used independently of TFLearn. Powerful helper functions to train any TensorFlow graph, with support of multiple inputs, outputs, and optimizers. Easy and beautiful graph visualization, with details about weights, gradients, activations and more. The high-level API currently supports most of the recent deep learning models, such as Convolutions, LSTM, BiRNN, BatchNorm, PReLU, Residual networks, Generative networks.

A hybrid front-end seamlessly transitions between Gluon eager imperative mode and symbolic mode to provide both flexibility and speed. Scalable distributed training and performance optimization in research and production is enabled by the dual parameter server and Horovod support. Deep integration into Python and support for Scala, Julia, Clojure, Java, C++, R and Perl. A thriving ecosystem of tools and libraries extends MXNet and enables use-cases in computer vision, NLP, time series and more. Apache MXNet is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator. Incubation is required of all newly accepted projects until a further review indicates that the infrastructure, communications, and decision-making process have stabilized in a manner consistent with other successful ASF projects. Join the MXNet scientific community to contribute, learn, and get answers to your questions.

Amazon EC2 G5 instances are the latest generation of NVIDIA GPU-based instances that can be used for a wide range of graphics-intensive and machine-learning use cases. They deliver up to 3x better performance for graphics-intensive applications and machine learning inference and up to 3.3x higher performance for machine learning training compared to Amazon EC2 G4dn instances. Customers can use G5 instances for graphics-intensive applications such as remote workstations, video rendering, and gaming to produce high-fidelity graphics in real time. With G5 instances, machine learning customers get high-performance and cost-efficient infrastructure to train and deploy larger and more sophisticated models for natural language processing, computer vision, and recommender engine use cases. G5 instances deliver up to 3x higher graphics performance and up to 40% better price performance than G4dn instances. They have more ray tracing cores than any other GPU-based EC2 instance.

Amazon EC2 P4d instances deliver high performance for machine learning training and high-performance computing applications in the cloud. Powered by NVIDIA A100 Tensor Core GPUs, they offer industry-leading throughput and low-latency networking, supporting 400 Gbps instance networking. P4d instances provide up to 60% lower cost to train ML models, with an average of 2.5x better performance for deep learning models compared to previous-generation P3 and P3dn instances. Deployed in hyperscale clusters called Amazon EC2 UltraClusters, P4d instances combine high-performance computing, networking, and storage, enabling users to scale from a few to thousands of NVIDIA A100 GPUs based on project needs. Researchers, data scientists, and developers can utilize P4d instances to train ML models for use cases such as natural language processing, object detection and classification, and recommendation engines, as well as to run HPC applications like pharmaceutical discovery and more.

Combining Data Science, Business Intelligence, and Data Management Capabilities in One Integrated, Self-Serve Platform. Analance is a robust, salable end-to-end platform that combines Data Science, Advanced Analytics, Business Intelligence, and Data Management into one integrated self-serve platform. It is built to deliver core analytical processing power to ensure data insights are accessible to everyone, performance remains consistent as the system grows, and business objectives are continuously met within a single platform. Analance is focused on turning quality data into accurate predictions allowing both data scientists and citizen data scientists with point and click pre-built algorithms and an environment for custom coding. Company – Overview Ducen IT helps Business and IT users of Fortune 1000 companies with advanced analytics, business intelligence and data management through its unique end-to-end data science platform called Analance.