Deci’s content hub on the latest Artificial Intelligence and Deep Learning trends and insights

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    Webinar: How to Select the Right CNN Architecture for Your Application

    Take a deep dive into the intricate but critical stage of selecting convolutional neural network (CNN) architectures.

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    Deci Launches Version 2.0 of its Deep Learning Development Platform, to Democratize the Power of NAS and Eliminate the AI Efficiency Gap

    Deci’s platform enables AI developers to build, optimize and deploy accurate, fast, and efficient computer vision models on any hardware.

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    MLPerf: Intel and Deci Boost NLP Models – Reaching Faster and More Accurate Inference Performance

    Deci’s DeciBert Models coupled with Intel's OpenVINO toolkit deliver better speed than BERT-Base, and higher accuracy than BERT-Large.

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    MLPerf: How Deci and Intel Achieved up to 16.8x Throughput Increase and +1.74% Accuracy Improvement

    This marks another significant milestone in the ongoing Deci-Intel collaboration towards enabling deep learning outstanding inference on CPUs

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    AMA: How to Productize Deep Learning Models

    Learn best practices for productizing your deep learning models, and successfully deploying them to production.

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    SuperGradients – The Simplest Way to Build Deep Learning Models

    Looking to train your deep learning models? SuperGradients is an open source training library with SOTA models with unbeatable accuracy. Learn more here.

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    Deci Boosts Computer Vision & NLP Models’ Performance at MLPerf

    Deci’s submissions deliver breakthrough accuracy & throughput performance across CV and NLP models on Intel’s CPUs.

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    13 Free Resources and Model Zoos for Deep Learning and Computer Vision Models

    Starting a deep learning project? Here are 13 free resources and model zoos for computer vision models that can help simplify your work.

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    Overview and Comparison of Neural Network Training Libraries

    Explore common PyTorch-based neural network training libraries and tools before selecting the most appropriate one for your use case.

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