Joe Fernandez
Staff Technical Writer, TensorFlow And Google ML
San Jose, California, United States
Summary
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RockstarJoe Fernandez is a Staff Technical Writer at Google focused on TensorFlow and Google ML with 15 years in tech, based in San Jose. He blends deep documentation craft with hands-on contributions to major open-source codebases, improving TensorFlow tutorials and clarifying Android Open Accessory protocol links across the AOSP mirror. His work emphasizes making complex ML concepts accessible to beginners through clear quickstart guides and practical data-augmentation tutorials. Comfortable across the stack, he moves between technical writing and lightweight development tasks, ensuring docs stay accurate as code evolves. Colleagues rely on him to translate research and APIs into usable learning paths that accelerate adoption by developers and students alike.
15 years of coding experience
Github Skills (7)
user-manual10
tensorflow10
learn-ruby-on-rails10
basics10
documentation10
redirect10
keras9
Programming languages (7)
Github contributions (5)
aosp-mirror/platform_development
Jun 2012 - Mar 2015
Role in this project:
Full-stack Developer
Full-stack DeveloperContributions:57 commits in 2 years 9 months
Contributions summary:Joe's contributions primarily involved adding redirects for Android Open Accessory protocol content to source.android.com. These changes are spread across multiple files and show work in various parts of the AOSP repository including tools and documentation. This indicates the user is involved in maintaining and improving documentation and potentially addressing outdated links.
tensorflow/docs
Sep 2021 - Jul 2022
TensorFlow documentation
Role in this project:Technical Writer
Technical WriterContributions:4 reviews, 11 commits, 1 PR in 10 months
Contributions summary:Joe primarily contributed to the documentation of TensorFlow tutorials, specifically the beginner quickstart guide. Their commits involved adding introductory sections, section headings, and making other minor fixes. The user also updated a tutorial on data augmentation. The commits indicate a focus on improving the clarity, structure, and overall presentation of the TensorFlow documentation.
deep-learningmachine-learningdeep-neural-networkstensorflow-tutorialstensorflow
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