Generate Synthesized Data That Looks Like the Real Thing

Fully Connected – a series where Chris and Daniel keep you up to date with everything that’s happening in the AI community.

This week we discuss BERT, a new method of pre-training language representations from Google for natural language processing (NLP) tasks. Then we tackle Facebook’s Horizon, the first open source reinforcement learning platform for large-scale products and services. We also address synthetic data, and suggest a few learning resources.

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Featuring:

• Chris Benson – Website, GitHub, LinkedIn, X
• Daniel Whitenack – Website, GitHub, X

Show Notes:

News/Discussion:

• Is artificial intelligence set to become art’s next medium?
• BERT (a new method for obtaining rich contextual language representations during pre-training):
  • Google research article
  • TensorFlow BERT
  • BERT paper
  • PyTorch BERT
  • NY Times article
  • Example Colab notebook
  • BERT explained article
  • Transformer paper
  • Google Open Sources BERT to Train Natural Language Models Without Breaking the Bank
• Horizon: The first open source reinforcement learning platform for large-scale products and services
• Does Synthetic Data Hold The Secret To Artificial Intelligence?
• AI Experts: Moving forward with AI likely a series of small steps, not giant leaps

Learning resources:

• The Backpropagation Algorithm Demystified

Books

• “Grokking Deep Learning” by Andrew Trask

Something missing or broken? PRs welcome!