Deep learning with TensorFlow (Day 2)

Location: Park Suite

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

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

Description

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.

Outline

Day 2

Deep neural networks
Variational autoencoders
Convolutional neural networks
Adversarial noise
DeepDream
Recurrent neural networks

Prerequisite knowledge

Familiarity with Python, matrices, modeling, and statistics

What you'll learn

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