Implementing Image Recognition Systems with TensorFlow

TensorFlow is popular a library for implementing a range of deep learning solutions but is especially useful for solutions that deal with images. This course will teach you the basics of how to use TensorFlow to implement the most typical scenarios.

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.

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