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 Apache Spark and BigDL, with Keras and TensorFlow support

Rich Ott (The Data Incubator)
9:00am–5:00pm
Sunday, April 29 through Monday, April 30
Location: Chelsea at Sheraton

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.

BigDL is a powerful tool for leveraging Hadoop and Spark clusters for deep learning. Rich Ott offers an overview of BigDL’s capabilities through its Python interface, exploring BigDL's components and explaining how to use it to implement machine learning algorithms. You'll use your newfound knowledge to build algorithms that make predictions using real-world datasets.

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

  • Understand where BigDL and Math Kernel Library (MKL) fit in the Spark ecosystem
  • Learn how to write and execute deep learning algorithms as Spark applications using BigDL and how to leverage existing models by importing from TensorFlow, Caffe, Torch, and Keras

Prerequisites:

  • A basic understanding of Python and PySpark, matrices, linear algebra, modeling, and machine learning

The BigDL library provides users with the ability to run deep learning applications on the Apache Spark framework while leveraging Math Kernel Library (MKL)—which consists of optimized mathematical operations that constitute the basis of machine learning algorithms—to boost performance. BigDL is ideal for training complex networks on large, distributed datasets on commodity CPUs and allows you to extend your existing work by importing models from TensorFlow, Caffe, Torch, and Keras.

Rich Ott offers an overview of BigDL’s capabilities through its Python interface, exploring BigDL’s components and explaining how to use it to implement machine learning algorithms. You’ll use your newfound knowledge to build algorithms that make predictions using real-world datasets. These include simple linear and logistic regression models as well as deep learning algorithms like multilayer perceptron networks, convolutional neural networks, recurrent neural networks, and autoencoders.

About your instructor

Photo of Rich Ott

Richard Ott is a data scientist in residence at the Data Incubator, where he gets to combine his interest in data with his love of teaching. Previously, he was a data scientist and software engineer at Verizon. Rich holds a PhD in particle physics from the Massachusetts Institute of Technology, which he followed with postdoctoral research at the University of California, Davis.

Conference registration

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

Comments on this page are now closed.

Comments

Picture of Rich Ott
Rich Ott | DATA SCIENTIST IN RESIDENCE
04/26/2018 9:08am EDT

Just a web browser, we’ll be running everything on a remote server. You should be aware that Safari sometimes has problems, we tend to have better luck with Chrome

Picture of Serhat Keçici
Serhat Keçici | SENIOR DATA SCIENTIST
04/25/2018 9:55pm EDT

Do we need to have any software installed to our laptops before coming to the training?

Picture of Rich Ott
Rich Ott | DATA SCIENTIST IN RESIDENCE
04/10/2018 1:39pm EDT

We will be offering this training at other O’Reilly conferences, if you’d like to attend another time.

Picture of Rich Ott
Rich Ott | DATA SCIENTIST IN RESIDENCE
04/10/2018 1:36pm EDT

Majid, unfortunately, we cannot give the material to anyone except those that attend the course

Picture of Majid Shaalan
Majid Shaalan | DIRECTOR OF COMPUTER & INFORMATION SCIENCES PROGRAM
04/08/2018 4:45pm EDT

I have the Platinum package and would love to attend. But, I won’t be able to as I will be in another one same time both days. Can get the training material/ resources for this?