From charlesreid1


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

Example TensorFlow Networks

This section contains a list of TensorFlow networks that have writeups on the wiki.

TensorFlow Models Repository

Writeups of models from


TensorFlow/MNIST - illustrating a basic convolutional network to do handwritten digit classification

TensorFlow/MNIST2 - a more, uh, convoluted example of how to do convolutional neural networks (lower-level, manual initialization and layer assembly, etc.)

Adversarial Neural Networks

TensorFlow/Adversarial Crypto - a triplet adversarial neural network configuration (encoder/decoder/eavesdropper networks corresponding to Alice/Bob/Eve).

Research Networks

TensorFlow/Differential Privacy - a research neural network configuration intended to help protect the privacy of users in crowdsourced data sets. Main challenge is in minimizing a non-convex loss function.

Usage and Components

Command Line Arguments

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


The embedding projector site is a page that uses (?) TensorFlow to create lower-dimensional visualizations from higher-dimensional data. There are several visualization options, including tSNE and PCA.


The Influence of Google Technologies

An interesting blog post that highlights the influence of Google Technologies on TensorFlow:

This includes:

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


My Repositories

Other Repositories

Official Repositories

  • - 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
  • - 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
  • - a library for doing transforms on input data for preprocessing (e.g., with Apache Beam)
    • getting started guide:
    • import the library as import tensorflow_transform as tft
    • define transforms as x_centered = x - tft.mean(x), where x is a Tensor object
    • the preprocessing function is passed Tensors in batches, not individually; operations/functions are applied to single tensors at a time; these operations are then "broadcast" to the entire tensor

Other Repositories