This repo contains materials for use in a TensorFlow workshop.
Contributions are not currently accepted. This is not an official Google product.
This document points to more information for each workshop lab.
- Building a small starter TensorFlow graph
- XOR: A minimal training example
- A LinearRegressor example that uses Datasets.
- Introducing MNIST, and building a simple linear classifier in TensorFlow.
- Using TensorFlow's high-level APIs to build an MNIST DNN Classifier, and introducing TensorBoard.
- Building custom
Estimators for a version of MNIST that uses CNNs, using either TensorFlow or Keras layers.
-
Transfer learning: using a trained model to 'bootstrap' learning new classifications.
- Using Cloud ML
- (possibly outdated) Using a custom Estimator
-
(possibly outdated) Building a word2vec model using a Custom Estimator, and exploring the learned embeddings. Introducing TFRecords.
In addition, there is an extras directory, that contains some older labs not currently used in this workshop (and which may not necessarily run with the latest version of TF), but which may be of interest.