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Hi there šŸ‘‹

  • šŸ”­ Iā€™m currently working on bringing together IoT and Machine Learning at scale. From the ultra-low-power edge (TinyML) to the clouds (AWS and Azure so far)
  • šŸŒ± Iā€™m learning a bunch of things, because the topic above is very wide! All what I learn, I try to bring back to the community. Therefore I am so happy that I was recognized as AWS Community Builder and offered to become a Mentor in the epic Deep Learning Specialization in Coursera from Prof. Andre Ng. I also volunteer in several other communities providing advice and counseling as Certified Mentor at The Mentoring Club, Microsoft Learn Student Ambassador and Github Campus Expert.
  • šŸ‘Æ Iā€™m looking to collaborate on anything related to IoT devices doing inferencing with optimized deep learning models and delivering reliable cloud services at scale (e.g. AWS SageMaker Neo, Apache TVM, TensorFlow Lite Micro...).
  • šŸ¤” Iā€™m looking for experts in the domain who are interested in public speaking (e.g. TinyML talks, where I am one of the organizers) or simply exchanging experiences and networking.
  • āš” If you didn't hear about TinyML before and I have raised your interest, you can do the Hands-on TinyML course at the EC-Council, which I authored or watch for free one of our latest TinyML talks to see if this is something interesting for you:

Youtube video

  • šŸ’¬ Ask me about my favourite food and I will get in trouble
  • šŸ“« How to reach me: both LinkedIn and GitHub work fine for me

Carlos Hernandez-Vaquero's Projects

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A curated list of awesome TensorFlow Lite models, samples, tutorials, tools and learning resources.

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Run VSCode (codeserver) on Google Colab or Kaggle Notebooks

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This is an application designed using micro-services to simplify connecting people at conferences by using their location data

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Labs for Course DP-100: Designing and Implementing Data Science Solutions on Microsoft Azure

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Open deep learning compiler stack for cpu, gpu and specialized accelerators

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Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more

prepack-performance icon prepack-performance

This repository aims to demonstrate the performance improvements obtained with PrePack, a partial evaluator for JavaScript

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Microservices Project template with Prometheus, Grafana, Node-Exporter, Backend and Frontend

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