Presented By O’Reilly and Intel AI
Put AI to Work
April 29-30, 2018: Training
April 30-May 2, 2018: Tutorials & Conference
New York, NY

In-Person Training
Deep learning with TensorFlow

Dylan Bargteil (The Data Incubator)
9:00am–5:00pm
Sunday, April 29 through Monday, April 30
Location: Morgan Level: Intermediate
SOLD OUT

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

TensorFlow is an increasingly popular tool for deep learning. Dylan Bargteil 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, Dylan 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. Dylan Bargteil 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 such as 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, Dylan covers several real-world deep learning applications, including machine vision, text processing, and generative networks.

About your instructor

Photo of Dylan Bargteil

Dylan Bargteil is a data scientist in residence at the Data Incubator, where he works on research-guided curriculum development and instruction. Previously, he worked with deep learning models to assist surgical robots and was a research and teaching assistant at the University of Maryland, where he developed a new introductory physics curriculum and pedagogy in partnership with HHMI. Dylan studied physics and math at University of Maryland and holds a PhD in physics from New York University.

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Conference registration

Get the Platinum pass or the Training pass to add this course to your package.

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Comments

Picture of Jason Perdue
Jason Perdue | SPEAKER MANAGER
01/10/2018 7:08am EST

The conference takes place at the New York Hilton Midtown. Clinton is the name of the room this training is scheduled to be in. Please check the room name on the day of the training as it may change.

Asha Mahesh
01/10/2018 6:27am EST

Can you please confirm the actual address of training location. It says Clinton. where in Clinton.