Running images through deep learning models is potentially the most typical scenario in which deep learning is used today. In this course, Implementing Image Recognition Systems with TensorFlow 1, you will learn the basics of how to implement a solution for the most typical deep learning imaging scenarios. First, you will learn how to pick a TensorFlow model architecture if you can implement your solution with pre-existing, pre-trained models. Next, you will learn how to extend such models using your own training images by taking advantage of transfer learning. Finally, you will see how to use more advanced solutions to do more advanced processing on images, like segmentation, and even learn how to implement a facial recognition solution. When you are finished with this course, you will have the skills and knowledge of TensorFlow and imaging in order to implement your own solutions successfully.
Author Name: Jon Flanders
Author Description:
Although Jon spent the first few years of his professional life as an attorney, he quickly found chasing bits more interesting than chasing ambulances. He first worked at the University of Minnesota, building a financial reporting Web site using ASP and SQL Server, specializing in automatic integration between multiple data stores. Since joining the training industry in 1999, Jon has devoted his time to working on various projects while migrating from the world of ASP and COM to the world of .NE… more

Table of Contents

• Course Overview
  1min
• Introduction
  15mins
• Picking and Using a Model
  26mins
• Transfer Learning
  36mins
• Localization and Segmentation
  22mins
• Face Recognition
  14mins