This module introduces advanced Keras capabilities for deep learning model development. It covers the Keras Functional API and Subclassing API for designing complex model architectures, creating custom layers to extend Keras functionality, and integrating Keras with TensorFlow 2.x for enhanced capabilities. Students will learn to implement these advanced techniques through hands-on labs and practical exercises.

This module focuses on developing sophisticated Convolutional Neural Networks using Keras. Students will learn advanced CNN architectures, data augmentation techniques to improve model generalization, and transfer learning with pre-trained models. The module also explores TensorFlow for image processing and introduces transpose convolution for tasks like image segmentation and generation, providing practical implementation experience through labs.

This module covers implementing Transformer architectures using Keras for sequential data processing. Students will learn to build and optimize Transformer models for various applications, including time series prediction and text generation. The module explores advanced Transformer applications and how to leverage TensorFlow for efficient sequential data handling, with hands-on labs to implement these powerful models.

This module explores unsupervised learning techniques and generative modeling in Keras. Students will build autoencoder architectures for representation learning, implement cutting-edge diffusion models, and develop Generative Adversarial Networks (GANs) for creating synthetic data. The module also covers how to use TensorFlow for advanced unsupervised learning tasks, with practical implementation through guided labs.

This module focuses on optimizing Keras models through advanced techniques. Students will implement custom training loops for fine-grained control over the training process, perform hyperparameter tuning using Keras Tuner, and explore model optimization strategies. The module also covers how to leverage TensorFlow for enhanced model optimization and custom workflows, with hands-on experience through practical labs.

This module introduces reinforcement learning concepts and implementation using Keras. Students will learn about Q-Learning algorithms and their implementation in Keras, and develop Deep Q-Networks (DQNs) for solving complex reinforcement learning problems. The module provides hands-on experience building reinforcement learning agents through practical labs and exercises.

This module allows students to apply their acquired knowledge in a comprehensive project. Students will implement transfer learning for image classification tasks, first practicing with fruit classification and then completing a final project on waste product classification. The module includes both guided practice and independent work, culminating in a peer-reviewed assignment to demonstrate mastery of the course concepts.

This concluding module summarizes the key concepts and skills learned throughout the course. It includes a final video wrapping up the course material, information on next steps for continuing education in deep learning, and acknowledgments from the course team. Students receive guidance on how to apply their new skills and where to find additional resources for further development.