Presented By
O’Reilly + Intel AI
Put AI to Work
April 15-18, 2019
New York, NY

In-Person Training
Deep Learning with TensorFlow

Dylan Bargteil (The Data Incubator)
Monday, April 15 & Tuesday, April 16,
9:00am - 5:00pm
Implementing AI
Location: Madison
Secondary topics:  Deep Learning and Machine Learning tools

Participants should plan to attend both days of this 2-day training course. To attend training courses, you must register for a Platinum or Training pass; does not include access to tutorials on Tuesday.

The TensorFlow library provides for the use of computational graphs, with automatic parallelization across resources. This architecture is ideal for implementing neural networks. This training will introduce TensorFlow's capabilities in Python. It will move from building machine learning algorithms piece by piece to using the Keras API provided by TensorFlow with several hands-on applications.

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

Participants will understand:

  • What machine learning, neural networks, deep learning, and artificial intelligence are.
  • What TensorFlow is and what applications it is good for.

Participants will be able to:

  • Create deep learning models for classification and regression using TensorFlow.
  • Evaluate the benefits and disadvantages of using TensorFlow over other machine learning software.

This training is for you because...

  • You are a software engineer or programmer with a background in Python, and you wish to develop an understanding of machine learning.
  • You have experience modeling or have a background in data science, and you would like to learn TensorFlow and deep learning.
  • You are in a non-technical role, and you would like to more effectively communicate with the engineers and data scientists in your company about TensorFlow and neural networks.

Prerequisites:

  • Familiar with Python language
  • Familiarity with matrices
  • Familiarity with modeling
  • Familiarity with statistics
  • No experience with TensorFlow is required

Hardware and/or installation requirements:

None

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

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.

Twitter for thedatainc

Conference registration

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Comments

Picture of Dylan Bargteil
Dylan Bargteil | DATA SCIENTIST IN RESIDENCE
03/14/2019 7:20am EDT

There is no specific required reading prior to the training, but familiarity with Python (particularly NumPy) is critical and familiarity with basic calculus, linear algebra, and machine learning concepts is helpful, though not required.

Ariela Shofaro | PRINCIPAL SOFTWARE ENGINEER
03/12/2019 1:17pm EDT

Is there any reading required prior to this training?