From charlesreid1

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[[TensorFlow/Command Line Args]] - notes on using command line arguments in TensorFlow models
[[TensorFlow/Command Line Args]] - notes on using command line arguments in TensorFlow models
==The Influence of Google Technologies==
An interesting blog post that highlights the influence of Google Technologies on TensorFlow: http://planspace.org/20170313-tensorflow_use_of_google_technologies/
This includes:
* gflags
* apputils (now abseil)
* bazel
* protobuf
* grpc
* gemmlowp
* gfile


=Github=
=Github=

Revision as of 05:20, 26 October 2017

Notes

Google Cloud Data Engineering Certification Course

Building TensorFlow Models: GCDEC/Building_Tensorflow/Notes

Deploying TensorFlow Models: GCDEC/Deploying_Tensorflow/Notes

Engineering TensorFlow Models: GCDEC/Engineering_Tensorflow/Notes

Usage and Components

Command Line Arguments

TensorFlow/Command Line Args - notes on using command line arguments in TensorFlow models

The Influence of Google Technologies

An interesting blog post that highlights the influence of Google Technologies on TensorFlow: http://planspace.org/20170313-tensorflow_use_of_google_technologies/

This includes:

  • gflags
  • apputils (now abseil)
  • bazel
  • protobuf
  • grpc
  • gemmlowp
  • gfile

Github

My Repositories

Other Repositories

Official Repositories

  • https://github.com/GoogleCloudPlatform/training-data-analyst - models and examples for the TensorFlow section of the Google Cloud Data Engineering training
    • relevant code is in courses/machine_learning/
    • contains sequential buildup of a simple tensorflow example into a more complex tensorflow example in courses/machine_learning/tensorflow
    • contains example bundled app for training/predictions using Cloud ML Engine in courses/machine_learning/cloudmle
  • https://github.com/GoogleCloudPlatform/cloudml-samples - examples that demonstrate how to use Cloud ML Engine; this demonstrates multiple techniques:
    • training a Keras model using Cloud ML Engine
    • using "canned" TensorFlow estimator
    • using custom TensorFlow estimator
    • using low-level TensorFlow API
    • both linear and deep models
    • wide and deep network

Other Repositories

Flags