TensorFlow™ is an open source software library for numerical computation using data flow graphs. TensorFlow was originally developed by researchers and engineers working on the Google Brain Team within Google's Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well.
In the first part of the tutorial, I will introduce the basic concepts of TensorFlow. What are TensorFlow data flow graphs and why do we need them? I will show how you can build your own computation graph and implement a linear classifier with low-level TensorFlow primitives. I will also motivate higher-level TensorFlow abstractions, that will increase your productivity, especially for Machine Learning.
In the second part, there will be a hands-on tutorial, in which you will be able to implement your own linear, as well as neural network classifier for recognizing handwritten digits using the higher-level TensorFlow Estimator API.
The goal of this tutorial is that you will:
- Understand the basic concepts of TensorFlow computation graphs, such as tensors, operations, sessions.
- Be able to judge if TensorFlow is the right tool for your problem.
- Attain an overview of higher-level TensorFlow APIs that can increase your productivity.
- Gain hands-on experience in deep learning by implementing a neural network classifier using TensorFlow estimator API.
Stephan Wolf is Senior Machine Learning Engineer in Google Research Europe. In his daily job, he consults product teams in implementing cutting-edge Machine Learning algorithms. Before, he was one of the core engineers in prototyping the Google Assistant that has launched on millions of devices. He is a former graduate of the Software Engineering elite graduate program at TUM, LMU and University Augsburg.