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Showing 2 open source projects for "checkpoint"
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Connect with customers in one appDialpad Connect is an AI-powered unified communications platform that combines voice, video, and messaging to enhance team collaboration and customer interactions. It features real-time call transcription, automated call summaries, and AI-generated action items to help users stay focused during conversations. The platform integrates seamlessly with popular business apps like Salesforce, Zendesk, Microsoft Teams, and Google Workspace to streamline workflows. Designed for businesses of all sizes, Dialpad Connect delivers enterprise-grade reliability with 100% uptime SLA and robust disaster recovery. Security and privacy are core priorities, meeting standards like GDPR, HIPAA, and SOC 2 compliance. Dialpad Connect helps companies elevate customer experiences while boosting team productivity.
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Project Planning and Management Software | PlanviewPlanview® Portfolios enables enterprises to accelerate strategic execution by seamlessly integrating business and technology planning, optimizing resources, and leveraging the power of embedded AI — Planview Anvi™ — to deliver breakthrough products, services, and customer experiences. This unified approach aligns strategy with execution, driving enhanced business performance across the organization.
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Simple StyleGan2 for Pytorch
Simplest working implementation of Stylegan2
...By default, if the training gets cut off, it will automatically resume from the last checkpointed file. Once you have finished training, you can generate images from your latest checkpoint. If a previous checkpoint contained a better generator, (which often happens as generators start degrading towards the end of training), you can load from a previous checkpoint with another flag. A technique used in both StyleGAN and BigGAN is truncating the latent values so that their values fall close to the mean. The small the truncation value, the better the samples will appear at the cost of sample variety. -
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GPT-2 FR
GPT-2 French demo | Démo française de GPT-2
...Create an environment then install the two packages pip install tensorflow==1.14 gpt-2-simple. A script and a notebook are available in the src folder to fine-tune GPT-2 on your own datasets. The output of each workout, i.e. the folder checkpoint/run1, is to be put ingpt2-model/model1 model2 model3 etc. You can run the script deploy_cloudrun.shto deploy all your different models (into gpt2-model) at once. However, you must have already initialized the gcloud CLI tool (Cloud SDK).
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