Best Deep Learning Software in India

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.

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.

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.