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In this module, you will dive into the practical implementation of convolutional neural networks for image classification tasks. Focusing on an agricultural land classification use case, you will build and train CNN models using Keras and PyTorch. Through hands-on labs, you鈥檒l gain experience in constructing and optimizing models in each framework. The module concludes with a comparative analysis of the two approaches, helping you understand the trade-offs in model performance, training efficiency, and deployment considerations. By the end of this module, you鈥檒l be equipped to choose the right framework for a given problem and justify your design decisions using real evaluation metrics.

In this module, you will explore vision transformers, a cutting-edge deep learning architecture originally developed for natural language processing and now transforming the field of computer vision. You will learn how to apply transfer learning to vision transformers for real-world image classification tasks. Using PyTorch and Keras frameworks, you鈥檒l implement, fine-tune, and compare vision transformer models in practical scenarios. You鈥檒l perform comparative evaluation between the vision transformer performance for Keras-based and PyTorch-based models.

In this module, you will make a final submission all the labs you鈥檝e completed throughout the course for evaluation. Your submission will be AI graded. Finally, we will wrap up the course by highlighting key takeaways and outlining next steps.