In this module, we will explore various pre-trained models, their architectures, and the principles of transfer learning. Through a series of detailed sessions, we will apply these concepts in practical settings, culminating in case studies and analytical discussions.

In this module, we will apply CNN techniques to real-world natural images, specifically focusing on flower images. Through an extensive case study spread over multiple sessions, we will learn to implement, evaluate, and refine models in a practical, industry-relevant context.

In this module, we will tackle the challenge of identifying medical abnormalities using CNNs. Focusing on X-Ray images, we will conduct a detailed case study over several sessions, learning to interpret medical data and develop effective diagnostic models.

In this module, we will introduce Recurrent Neural Networks, covering their basic concepts, architecture, and types. We will delve into training methods and address common challenges like the vanishing gradient problem through a series of detailed sessions.

In this module, we will focus on Long Short-Term Memory (LSTM) networks, covering their architecture and functionality. We will compare LSTM with other RNN variants like GRU and implement these networks in practical scenarios through a series of detailed sessions.

In this module, we will apply RNN techniques to develop a Part-Of-Speech tagger for natural language processing tasks. Through an extended case study spread across multiple sessions, we will develop, evaluate, and refine the performance of the Part-Of-Speech tagger.

In this module, we will delve into the practical application of RNNs for text generation by exploring a comprehensive code generator case study divided into four parts. Each part builds on the previous one, enhancing our understanding and skills in using RNNs for generating coherent text.