Presented By O’Reilly and Intel AI
Put AI to work
8-9 Oct 2018: Training
9-11 Oct 2018: Tutorials & Conference
London, UK

In-Person Training
Deep learning with TensorFlow

Robert Schroll (The Data Incubator)
9:00–17:00
Monday, 8 October through Tuesday, 9 October
Location: Hilton Meeting rooms 5/6 Level: Intermediate
Secondary topics:  Deep Learning tools

Participants should plan to attend both days of this 2-day training course. Platinum and Training passes do not include access to tutorials on Tuesday.

TensorFlow is an increasingly popular tool for deep learning. Robert Schroll offers an overview of the TensorFlow graph using its Python API. You'll start with simple machine learning algorithms and move on to implementing neural networks. Along the way, Robert covers several real-world deep learning applications, including machine vision, text processing, and generative networks.

What you'll learn, and how you can apply it

  • Understand TensorFlow’s strengths for machine learning and how TensorFlow can help with AI problems like object recognition and text processing
  • Learn how to build a basic computation within TensorFlow, using Python

Prerequisites:

  • Basic knowledge of Python
  • Familiarity with matrices, modeling, and statistics

Hardware and/or installation requirements:

  • A laptop (You'll be provisioned with a cloud instance with TensorFlow.)

Many of the deep learning algorithms used in AI applications are powered by large matrix operations. TensorFlow provides data flow graphs for such operations, allowing algorithms to be easily parallelized across multiple processors or machines. This makes TensorFlow an ideal environment for implementing neural networks and other deep learning algorithms.

Robert Schroll offers an overview of the TensorFlow graph using its Python API. You’ll start with simple machine learning algorithms and move on to implementing neural networks, including convolutional neural networks to provide object recognition for machine vision, recurrent neural networks, including long short-term memory architectures, that allow the comprehension of time series and language, and generative networks, which give AI applications the ability to create output. Along the way, Robert covers several real-world deep learning applications, including machine vision, text processing, and generative networks.

About your instructor

Photo of Robert Schroll

Robert Schroll is a data scientist in residence at the Data Incubator. Previously, he held postdocs in Amherst, Massachusetts, and Santiago, Chile, where he realized that his favorite parts of his job were teaching and analyzing data. He made the switch to data science and has been at the Data Incubator since. Robert holds a PhD in physics from the University of Chicago.

Conference registration

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