Google Cloud Speech-to-Text
An API powered by Google's AI technology allows you to accurately convert speech into text. You can accurately caption your content, provide a better user experience with products using voice commands, and gain insight from customer interactions to improve your service. Google's deep learning neural network algorithms are the most advanced in automatic speech recognition (ASR). Speech-to-Text allows for experimentation, creation, management, and customization of custom resources. You can deploy speech recognition wherever you need it, whether it's in the cloud using the API or on-premises using Speech-to-Text O-Prem. You can customize speech recognition to translate domain-specific terms or rare words. Automated conversion of spoken numbers into addresses, years and currencies. Our user interface makes it easy to experiment with your speech audio.
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ActCAD Software
ACTCAD is suitable for professional drawings creation for Architects, Structural Engineers, Civil Engineres, Mechanical Drawings, Electrical drawings, interior design, tool design, machine designs etc.ActCAD is professional grade 2D Drafting and 3D Modeling CAD software which works in dwg and dxf file formats. Most affordable cad software.ActCAD is a native dwg/dxf cad software suitable for professional 2D drafting and 3D modeling projects. ActCAD is trusted by over 30000 users in over 103 countries for more than 10 years. The interface, commands, icons, dialogs, shortcuts etc. are very much similar to other popular cad software tools available in market. Flexible license types available even for single license. There is no learning for existing cad users while saving 80% of the costs.ActCAD offers free email technical support without any limitations. ActCAD can be fully customized and programs can be developed using our free API toolkit. It supports popular programming languages like , lisp dcl, .net, C++ etc. Apart from all regular commands, ActCAD offers many productive tools like pdf to cad converter, Block libraries, Image to Cad converter, handling point sets between Cad and Excel and many more.
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AWS Neuron
It enables efficient training on Amazon Elastic Compute Cloud (Amazon EC2) Trn1 instances powered by AWS Trainium. Additionally, for model deployment, it facilitates both high-performance and low-latency inference utilizing AWS Inferentia-based Amazon EC2 Inf1 instances along with AWS Inferentia2-based Amazon EC2 Inf2 instances. With the Neuron SDK, users can leverage widely-used frameworks like TensorFlow and PyTorch to effectively train and deploy machine learning (ML) models on Amazon EC2 Trn1, Inf1, and Inf2 instances with minimal alterations to their code and no reliance on vendor-specific tools. The integration of the AWS Neuron SDK with these frameworks allows for seamless continuation of existing workflows, requiring only minor code adjustments to get started. For those involved in distributed model training, the Neuron SDK also accommodates libraries such as Megatron-LM and PyTorch Fully Sharded Data Parallel (FSDP), enhancing its versatility and scalability for various ML tasks. By providing robust support for these frameworks and libraries, it significantly streamlines the process of developing and deploying advanced machine learning solutions.
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Fabric for Deep Learning (FfDL)
Deep learning frameworks like TensorFlow, PyTorch, Caffe, Torch, Theano, and MXNet have significantly enhanced the accessibility of deep learning by simplifying the design, training, and application of deep learning models. Fabric for Deep Learning (FfDL, pronounced “fiddle”) offers a standardized method for deploying these deep-learning frameworks as a service on Kubernetes, ensuring smooth operation. The architecture of FfDL is built on microservices, which minimizes the interdependence between components, promotes simplicity, and maintains a stateless nature for each component. This design choice also helps to isolate failures, allowing for independent development, testing, deployment, scaling, and upgrading of each element. By harnessing the capabilities of Kubernetes, FfDL delivers a highly scalable, resilient, and fault-tolerant environment for deep learning tasks. Additionally, the platform incorporates a distribution and orchestration layer that enables efficient learning from large datasets across multiple compute nodes within a manageable timeframe. This comprehensive approach ensures that deep learning projects can be executed with both efficiency and reliability.
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