Deep learning with TensorFlow

Robert Schroll (The Data Incubator)

Monday, Sep 9 & Tuesday, Sep 10,
9:00am - 5:00pm

Location: Market (Hilton)

Secondary topics: Deep Learning, Deep Learning tools, Machine Learning

Average rating:

(5.00, 2 ratings)

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 computational graphs with automatic parallelization across resources, ideal architecture for implementing neural networks. Robert Schroll walks you through TensorFlow's capabilities in Python 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

By the end of this two-day training course, you'll understand:

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

And you'll 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

Who is this presentation for?

You're 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're in a nontechnical role, and you would like to more effectively communicate with the engineers and data scientists in your company about TensorFlow and neural networks.

Level

Intermediate

Prerequisites:

Familiarity with Python, matrices, modeling, and statistics

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

Robert Schroll is a data scientist in residence at the Data Incubator. Previously, he held postdocs in Amherst, Massachusetts, and Santiago, Chile, where he realized that his favorite parts of his job were teaching and analyzing data. He made the switch to data science and has been at the Data Incubator since. Robert holds a PhD in physics from the University of Chicago.