Amy Unruh walks you through the process of building a complete machine learning pipeline, covering ingest, exploration, training, evaluation, deployment, and prediction. Along the way, Amy explains how to explore and split large datasets correctly using BigQuery and Cloud Datalab.
Third-party libraries used:
Outline
Machine learning and TensorFlow
A wide and deep thought experiment
Wide and deep code model
Additional info
Diving into a lower level of TensorFlow
Creating a simple network by hand
Upgrading the model to a CNN (time permitting)
Wrap-up and Q&A
Amy Unruh is a developer programs engineer for the Google Cloud Platform, where she focuses on machine learning and data analytics, as well as other Cloud Platform technologies. Amy has an academic background in CS/AI, and she’s worked at several startups as well as industrial R&D and published a book on App Engine.
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Comments
hi Kate, that’s correct — CPU is fine.
Hi Amy: Just checking but we should be able to complete the labs using CPU (GPU not required), right? Thanks ~
Ashley, If you don’t have python installed, conda might be easier (https://conda.io/docs/user-guide/overview.html , https://conda.io/docs/user-guide/install/windows.html).
However, you’ll also be able to run almost everything using colab (colab.research.google.com), which is browser-based, so you can just go that route if you have installation issues.
I will bring a corporate laptop that has windows installed. How should I install virtualenv on top of it? Thanks.
Jim, that should be fine. You can just follow along, or maybe pair with someone nearby. All the code is on GitHub and you can try it later. You’ll also be able to run nearly everything on colab (http://colab.research.google.com)
I will not have a laptop to bring. In your opinion, Will this tutorial be useful without hands on? Thanks.