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
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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.
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)
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: