Expanded Library

Implement Image Captioning with Recurrent Neural Networks

by Abdul Rehman Yousaf

This course will teach you how to build and train image captioning models using TensorFlow, with the help of a case study - building a model for image tagging. You will learn how to prepare the data for model training and evaluate the trained model.

What you'll learn

Manually interpreting billions of images is time-consuming and almost impossible. But if we teach machines to understand images, this task will become much more efficient. In this course, Implement Image Captioning with Recurrent Neural Networks, you’ll learn to build and train image captioning models with RNNs using TensorFlow. First, you’ll explore the broader understanding of recurrent neural networks along with an overview of image captioning and how CNNs can help us to understand images. Next, you’ll discover how to prepare image and text data. Then, you'll learn how to develop a deep learning model for image captioning, and different options to evaluate that model using TensorFlow. Finally, you’ll understand the implementation of the data science process. When you’re finished with this course, you’ll have the skills and knowledge of RNNs and CNNs needed to build image captioning models.

About the author

Abdul Rehman is the founder of Pythonist.org, a software consulting, training, and application development company. Currently, he is working as a Senior Machine Learning Engineer at Nexthon Technologies where he built several amazing projects powered with artificial intelligence. Prior to that, he worked as a Cloud Solution Architect, building powerful, secure, and scalable infrastructures on various cloud vendors like Google Cloud Platform and AWS. During both of these roles, he used Python as ... more

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