Presented By O’Reilly and Cloudera
Make Data Work
21–22 May 2018: Training
22–24 May 2018: Tutorials & Conference
London, UK

Machine learning with TensorFlow (Day 2)

Dana Mastropole (The Data Incubator)
Location: Capital Suite 17

The TensorFlow library provides for the use of data flow graphs for numerical computations, with automatic parallelization across several CPUs or GPUs. This architecture makes it ideal for implementing neural networks and other machine learning algorithms. Dana Mastropole details TensorFlow’s capabilities through its Python interface, moving from building machine learning algorithms piece by piece to using the higher-level abstractions provided by TensorFlow. You’ll use this knowledge to build machine learning models on real-world data.

You’ll be provisioned a cloud instance with TensorFlow as a part of this course.

Outline

Day 1

  • Introduction to TensorFlow
  • Iterative algorithms
  • Machine learning
  • Basic neural networks

Day 2

  • Deep neural networks
  • Variational autoencoders
  • Convolutional neural networks
  • Adversarial noise
  • DeepDream
  • Recurrent neural networks
Photo of Dana Mastropole

Dana Mastropole

The Data Incubator

Dana Mastropole is a data scientist in residence at the Data Incubator and contributes to curriculum development and instruction. Previously, Dana taught elementary school science after completing MIT’s Kaufman teaching certificate program. She studied physics as an undergraduate student at Georgetown University and holds a master’s in physical oceanography from MIT.