In this module, you’ll delve into the core mechanisms of neural networks. You'll explain how models optimize gradient descent algorithms and explore backpropagation. Further, you’ll demonstrate how to address challenges using the vanishing gradient problem. Finally, this module introduces you to the activation functions as solutions. Through hands-on exercises, you’ll observe how different activation functions impact learning, equipping you with the knowledge to design and train effective deep learning models.

In this module, you will explore the applications of deep learning using the Keras library. You’ll also gain insights into the role of Keras and other deep learning libraries in model development. This module guides you through building and training regression and classification models using Keras. The hands-on labs in this module provide real-world datasets to implement and evaluate deep learning models for various predictive tasks.

In this module, you’ll delve into advanced deep learning architectures and techniques using the Keras library. You’ll distinguish between shallow and deep neural networks, understanding their respective complexities and applications. You’ll also explore convolutional neural networks (CNNs) for image processing tasks and gain guidance for implementing CNNs using Keras. You’ll explore recurrent neural networks (RNNs) for sequential data and transformer models that have revolutionized natural language processing (NLP). Additionally, you’ll explore autoencoders for unsupervised learning and pretrained models to enhance performance and reduce training time. The hands-on labs in this module provide you with a practical understanding of various deep learning models and transformers in Keras.

In this final module, you will apply and demonstrate the full range of skills you have gained throughout the course. In this module, you will consolidate your learning through a final project integrating core deep learning concepts such as image classification and caption generation using Keras. After completing the project, you will reflect on your journey through the course and understand the next steps for continued growth in deep learning.