Deep Learning Capstone Project

The project is best suited to learners who already have a solid foundation in deep learning and want to put those skills into practice. It is particularly relevant for aspiring AI engineers who want hands-on experience in designing, training, comparing, and evaluating models than just theory.

Yes, you will work with both frameworks. You will develop classifiers using Keras and PyTorch, then compare their performance. This gives you practical exposure to different deep learning workflows with an understanding of how framework choice and implementation can influence a models results.

Absolutely! Data handling is one of the first stages of the project. You will work with data loading and augmentation using both Keras and PyTorch. Strong data preparation matters as model performance depends heavily on how effectively the input data is organized, processed, and presented.

Yes. The course includes dedicated work with convolutional neural networks, including building classifiers with both Keras and PyTorch. You will apply CNNs to the agricultural land-classification problem, train and test models to compare their results, making the concept considerably more practical.

Vision transformers apply transformer-based approaches to visual data. You will explore vision transformers and transfer learning, implementing them with both Keras and PyTorch. You will then compare approaches and examine how transformer-based models can be integrated with special CNN workflows.

You will compare models using practical performance measures. The project includes model evaluation using metrics such as accuracy and inference time. You will also examine how architecture and preprocessing choices affect results, helping you make more informed decisions about model performance.

The course is specifically designed around a portfolio-worthy project. By completing the project, you can demonstrate experience with data handling, model development, comparison, evaluation, and advanced architectures. The completion of the project clearly reflects your practical learning experience.

Yes. This is an advanced course and not a beginner-level introduction. It is recommended to complete the relevant courses in the IBM AI Engineering Professional Certificate before starting this project. You should understand the core deep learning concepts and can work with Keras and PyTorch.