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’ll 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’ll 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’ll implement, fine-tune, and compare vision transformer models in practical scenarios. Through hands-on labs, you’ll deepen your understanding of these frameworks, evaluate model performance, and prepare to integrate transformer-based models into a complete deep learning pipeline. This module builds on your prior knowledge of convolutional neural networks and equips you with the skills to effectively use transformers in visual recognition tasks.

In this module, you will bring together all the skills and concepts you’ve learned by working on a real-world deep learning problem. You’ll perform comparative evaluation between the Vision transformer performance for Keras-based and PyTorch-based models. This hands-on experience is designed to help you deepen your understanding of applying transfer learning, model training workflows and prepare you for the final submission. Finally, wesum up the course learning and highlights key takeaways and next steps.