Image Captioning with TensorFlow & Streamlit
By External Provider
About this course
By completing this course, learners will be able to preprocess image and text datasets, build and evaluate a deep learning model, and deploy a fully functional image captioning application. They will gain hands-on experience in applying tokenization, feature extraction, CNN-RNN architectures, and BLEU score evaluation for accurate caption generation. This course uniquely bridges computer vision and natural language processing, enabling learners to generate meaningful captions for social media images. Unlike traditional AI tutorials, it not only covers dataset preparation and neural network modeling but also demonstrates how to create an interactive Streamlit app and deploy it on AWS EC2 for real-world accessibility. Learners benefit by acquiring both technical depth and practical deployment skills, preparing them for roles in AI development, machine learning engineering, and applied data science. By the end, they will confidently design, test, and launch their own automatic image captioning systems that integrate seamlessly into modern applications.
What you will learn
- Preprocess image/text datasets with tokenization and feature extraction.
- Build CNN-RNN models and evaluate performance with BLEU scores.
- Deploy a Streamlit image captioning app on AWS EC2 for real-world use.