Deep learning with TensorFlow

Michael Cullan (Pragmatic Institute)

Monday, 14 Oct & Tuesday, 15 Oct,
9:00-17:00

Location: Park Suite

Implementing AI

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

Average rating:

(4.00, 1 rating)

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. Michael Cullan 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 who wants to better understand 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.

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

Michael Cullan is a data scientist in residence at Pragmatic Institute, where he teaches hands-on courses in data science and business-oriented topics in managing data science initiatives at the organizational level. He also leads internal data science projects in support of marketing and operations teams. He earned a master’s degree in statistics and a bachelor’s degree in mathematics. His academic research areas ranged from computational paleobiology, where he developed software for measuring evidence for disparate evolutionary models based on fossil data, to music and AI, where he assisted in modeling musical data for a jazz improvisation robot. In his free time, he applies his math and programming skills toward creating code-based visual art and design projects.