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7.2: To the future and beyond - autoencoders, GANs, RNNs and more

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Congratulations on completing Google's Machine Learning for Web Developers and creatives course. From learning the basics of machine learning to creating a full end-to-end web app that can detect spam using a natural language model. You completed an in-depth introduction to Machine Learning with a JavaScript specific lens using TensorFlow.js to use Web ML right in the browser. This video wraps up your learnings with a review of the course outline, new model architectures to explore that build upon your current knowledge, and further resources worth checking out. Remember to ask questions on the forum to discuss or share ideas with your peers: https://discuss.tensorflow.org/tag/tfjs - ensure you tag with TFJS so we can find it! Finally for a chance to be featured at future events remember to use the hashtag WebML for any creations you make - we would love to see what you make with Machine Learning in JS!

Resources:
Codelabs →https://goo.gle/3RVWwhK
Github →https://goo.gle/3xaVLaP
Website / API → https://goo.gle/3jLoGzc
Models → https://goo.gle/3Yj4GTy
Github Code → https://goo.gle/3DZgCBP
Forum → https://goo.gle/3IbMcyS
Codepen→ https://goo.gle/3Xn0nFE
Glitch →https://goo.gle/3YCHRdi

Catch more episodes from Machine Learning for Web Developers (Web ML) → https://goo.gle/learn-WebML
Check out TensorFlow on YouTube → https://goo.gle/TensorFlow-YouTube
Subscribe to Google Developers → https://goo.gle/developers

Connect with Jason Mayes to ask questions:
LinkedIn → https://goo.gle/3GwgeLw
Twitter →https://goo.gle/3Xh6MT7
Discord →https://goo.gle/3WWVO5t

Use #WebML to share your learnings and creations from this course to meet your peers on social media!

See what others have already made with Web ML → http://goo.gle/made-with-tfjs
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Google, developers, machine learning for web developers
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