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Sep 4-5, 2018: Training
Sep 5-7, 2018: Tutorials & Conference
San Francisco, CA

In-Person Training
Deep learning with Apache Spark and BigDL, with Keras and TensorFlow support

Rich Ott (The Data Incubator)
9:00am-5:00pm
Tuesday, September 4 through Wednesday, September 5
Location: Continental 1
Secondary topics:  Computer Vision, Deep Learning tools
Average rating: *****
(5.00, 1 rating)

Participants should plan to attend training courses on both Tuesday and Wednesday. To attend training courses, you must register for a Platinum or Training pass; does not include access to tutorials on Wednesday.

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

Hardware and/or installation requirements:

  • A laptop with Google Chrome or Firefox installed (All training is done through a remote system.)

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 auto-encoders.

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.

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Comments

Picture of Rich Ott
Rich Ott | DATA SCIENTIST IN RESIDENCE
09/03/2018 7:33am PDT

I’m very sorry, I didn’t see the notification about this before. You only need to have a web browser, preferably Chrome or Firefox. Everything will be done on a remote system.

Vahid Sherkat | ARCHITECT
08/24/2018 9:08am PDT

Will a windows 7 laptop work for this training? Do we have pre-install BigDl prior to the course (or will we install during the coures)?