Note:
Workshop documents are available on Github
(full notebooks and slides).

This workshop gives a quick overview over Tensorflow, from basic concepts and low-level operations all the way through using predefined estimators and running the model in the cloud. The main part of the workshop will be spent on reading through carefully prepared example code and asking questions. This allows to cover a lot of material, but post-workshop efforts are certainly needed in order to master the content.

ML knowledge is not strictly required for understanding the Tensorflow code, but we won't have time to explain ML theory during
the workshop.

Participants will
- Data preparation for Tensorflow and create a sharded dataset
- Understand Tensorflow basics (graph, execution model and shapes)
- Connect data to estimator, use LinearClassifier, DNNClassifier
- Train and deploy model in the cloud

The workshop material covers many more topics (convolutions, Keras, recurrent networks) and workshop participants will be well-prepared to tackle these advanced topics after the workshop.

• Python knowledge
• Laptop with pre-installed Docker CE (some Windows users might need to use Docker Toolbox instead)
• After installing Docker execute the following command to fetch the workshop image: "docker pull andstein/tensorflow-basics"
• Make sure that you can run and access the notebook contents in the browser
• For users who fail to install Docker (sad!) we will provide a virtual machine during the workshop