Implementing Image Recognition Systems with TensorFlow

Course Overview

What skills and knowledge will you acquire during this course?
This course, Implementing Image Recognition Systems with TensorFlow, will provide learners with the skills and knowledge to successfully implement their own solutions. Learners will gain an understanding of how to pick a TensorFlow model architecture and how to extend pre-trained models using transfer learning. Additionally, learners will learn how to use more advanced solutions to do more advanced processing on images, such as segmentation, and how to implement a facial recognition solution. By the end of the course, learners will have a comprehensive understanding of TensorFlow and imaging, and the ability to implement their own solutions.

How does this course contribute to professional growth?
This course contributes to professional growth by teaching the basics of how to use TensorFlow to implement the most typical deep learning imaging scenarios. It covers topics such as picking a TensorFlow model architecture, extending pre-trained models using transfer learning, and using more advanced solutions for image processing and facial recognition. By the end of the course, learners will have the skills and knowledge of TensorFlow and imaging necessary to implement their own solutions successfully.

Is this course suitable for preparing further education?
This course is suitable for preparing further education in the field of deep learning and image recognition. It provides a comprehensive overview of the basics of using TensorFlow to implement the most typical scenarios, such as running images through deep learning models, extending pre-trained models with transfer learning, and using more advanced solutions for segmentation and facial recognition. With the skills and knowledge gained from this course, students will be well-prepared to pursue further education in the field.