Put AI to Work
April 15-18, 2019
New York, NY

In-Person Training
Deep learning with TensorFlow (SOLD OUT)

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

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. Dylan Bargteil walks you through TensorFlow's capabilities in Python, teaching you how to build machine learning algorithms piece by piece and use the Keras API provided by TensorFlow with several hands-on applications.

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

  • Learn what machine learning, neural networks, deep learning, and artificial intelligence are
  • Understand what TensorFlow is and what applications it's good for
  • 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're a software engineer or programmer with a background in Python, and you want to develop an understanding of machine learning.
  • You have experience modeling or have a background in data science, and you want to learn TensorFlow and deep learning.
  • You're in a nontechnical role, and you want to more effectively communicate with the engineers and data scientists in your company about TensorFlow and neural networks.

Prerequisites:

  • Familiarity with Python, matrices, modeling, and statistics
  • No experience with TensorFlow required

Outline

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

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

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Comments

Picture of Dylan Bargteil
Dylan Bargteil | DATA SCIENTIST IN RESIDENCE
04/14/2019 5:38pm EDT

Fair play. You should bring a laptop, as you will be reading and writing code.

Our cloud provider for this training is Digital Ocean.

Sarah Chen | VP
04/10/2019 3:19pm EDT

There has to be some hardware requirements. I have chrome on my phone. But is that enough? :)
Would it be possible to learn the name of the cloud provider?
Thanks.

Picture of Dylan Bargteil
Dylan Bargteil | DATA SCIENTIST IN RESIDENCE
04/10/2019 12:47pm EDT

There are no hardware requirements. The reason there are no software requirements is because we have provisioned environments for you on a cloud provider, thus all the required software is already and installed and running on provisioned hardware.

Sarah Chen | VP
04/10/2019 12:40pm EDT

Hello Dylan,
Is there any hardware requirement? May I please ask why there is no software required other than browser? Wouldn’t it be time saving for all if software are installed before coming to training?

Picture of Dylan Bargteil
Dylan Bargteil | DATA SCIENTIST IN RESIDENCE
04/09/2019 8:22am EDT

The only program you will need a web browser; we recommend using Chrome or Firefox for best compatibility with the JupyterHub platform we’ll be using.

Darren Capner | SOFTWARE DEVELOPER
04/09/2019 7:56am EDT

Hello. Is there anything that we need to bring installed on our computers to follow along in this training session?

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?