Put AI to work
June 26-27, 2017: Training
June 27-29, 2017: Tutorials & Conference
New York, NY

In-Person Training
Deep learning with TensorFlow

Robert Schroll (The Data Incubator), Michael Li (The Data Incubator), Dana Mastropole (The Data Incubator)
Monday, June 26 & Tuesday, June 27, 9:00am - 5:00pm
Location: Morgan
Secondary topics:  Deep Learning
See pricing & packages
Early Price ends May 12

This course will sell out—sign up today!

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

Robert Schroll, Michael Li, and Dana Mastropole demonstrate TensorFlow's deep learning capabilities through its Python interface as they walk you through building machine-learning algorithms piece by piece and implementing neural networks using TFLearn. Along the way, you'll explore 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
  • Learn how to build a basic computation within TensorFlow, using Python and how to use TFLearn’s machine-learning algorithms
  • Discover how TensorFlow can help with AI problems like object recognition and text processing to chat apps

Prerequisites:

  • Basic knowledge of Python

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: convolutional neural networks can be used to provide object recognition for machine vision; recurrent neural networks, including long short-term memory architectures, allow the comprehension of time series and language; and generative networks give AI applications the ability to create output. 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.

Robert Schroll, Michael Li, and Dana Mastropole demonstrate TensorFlow’s deep learning capabilities through its Python interface as they walk you through building machine-learning algorithms piece by piece and implementing neural networks using TFLearn. Along the way, you’ll explore several real-world deep learning applications, including machine vision, text processing, and generative networks.

About your instructors

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.

Photo of Michael Li

Tianhui Michael Li is the founder and CEO of the Data Incubator. Michael has worked as a data scientist lead at Foursquare, a quant at D.E. Shaw and JPMorgan, and a rocket scientist at NASA. At Foursquare, Michael discovered that his favorite part of the job was teaching and mentoring smart people about data science. He decided to build a startup that lets him focus on what he really loves. He did his PhD at Princeton as a Hertz fellow and read Part III Maths at Cambridge as a Marshall scholar.

Photo of Dana Mastropole

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.

Conference registration

Get the Platinum pass or the Training pass to add this course to your package. Early Price ends May 12.

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Comments

Salil Jain | SENIOR SECURITY ADVISOR
04/20/2017 10:14am EDT

I have prelim working understanding of TensorFlow. Will this training cover advanced topics to be worthwhile?

Picture of Robert Schroll
Robert Schroll | DATA SCIENTIST IN RESIDENCE
03/29/2017 9:50am EDT

We’ll discuss RNNs in general, and we’ll go through a particular example using an LSTM network.

Jonathan Corey | ANALYTICS MODELER
03/29/2017 8:32am EDT

Will this training include the use of RNNs on time series data or is it mentioned just as a possible application?