This full-day course will provide you with the hands-on knowledge you need to develop deep learning computer vision applications—both on embedded systems and in the cloud—with TensorFlow, one of today's most popular frameworks for deep learning.


Sept 7, 2017
Hamburg, Germany
Steigenberger Hotel

Sponsored by

 
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WHO SHOULD ATTEND?

If you’re developing deep learning-based computer vision applications and want to use TensorFlow to do it, this course is for you! The only pre-requisites are a good understanding of Python (e.g., basic syntax and structure) and a basic knowledge of deep learning concepts. 


COURSE STRUCTURE

MORNING

Introduction to TensorFlow

  • What It Is, What It’s Used For

  • Graphs and Tensors

  • Basics of Building Graphs in TensorFlow

Neural Networks in TensorFlow

Exercises including: Linear Regression, Logistic Regression (Recognition of Hand-Written Digits with MNIST Database)

 

AFTERNOON

Object Recognition in TensorFlow

  • Quick Refresher on Neural Networks: Shallow, Deep, and Convolutional
  • Exploration of CNN Model for MNIST Demo

Training Data and Issues

Open Source CNN Models

Exercises Including: Deep Model for MNIST, TensorBoard Visualization Tool, Convolutional (CNN) MNIST model


What Do I Need to Know Before I Show Up?

The class covers deep learning for computer vision applications using TensorFlow. We assume that:

  • You know the basics of deep learning algorithms and concepts for computer vision, including convolutional neural networks
  • You know the basics of the Python programming language
  • You do not know TensorFlow

To make sure you’re up to speed on Python, please review sections 1-5 of this online Python tutorial before class.

To make sure you’re up to speed on deep learning algorithms and concepts, please watch these two excellent videos before class:

Demystifying Deep Neural Networks 
by Shehrzad Qureshi with BDTI

 


Seats are limited—register today!

 

Sept 7, 2017
Hamburg, Germany
Steigenberger Hotel

 


New training dates and locations will be announced soon!  
Sign up to receive updates on future trainings.