In this module, we will delve into the specialized field of multi-target regression using deep learning. We will cover the theoretical foundations and follow a step-by-step coding guide to implement and refine regression models capable of predicting multiple continuous variables simultaneously.

In this module, we will embark on a comprehensive journey into classification with deep learning, focusing on binary and multi-label classification techniques. We will build, code, and refine models that can effectively classify data into distinct or multiple categories, using hands-on labs and practical examples.

In this module, we will dive deep into Convolutional Neural Networks (CNNs), from their basic architecture to advanced applications. We will engage with interactive explorations, hands-on labs, and practical exercises to develop a robust understanding of CNNs' role in image recognition, classification, and semantic segmentation.

In this module, we will explore the fascinating world of Autoencoders, focusing on their theoretical foundations and practical applications. We will learn how to effectively implement Autoencoders, understand their diverse uses, and gain hands-on experience through coding labs.

In this module, we will delve into transfer learning and pretrained models, exploring how these techniques revolutionize the efficiency and effectiveness of deep learning. We will learn to apply these methods practically through lab sessions, significantly enhancing our deep learning projects.

In this module, we will explore Recurrent Neural Networks (RNNs) and their application in processing sequential data. We will focus on Long Short-Term Memory (LSTM) networks for time series prediction, gaining practical experience through coding labs and hands-on experimentation.

In this module, we will explore Shiny, a framework for building interactive web applications. We will learn about its essential components, delve into language selection and reactive expressions, and gain hands-on experience in developing and deploying Shiny apps for real-world use.