Introduction to RNN and DNN

Artificial Intelligence is transforming industries by enabling machines to learn from data and make intelligent decisions. This course offers an in-depth exploration of Recurrent Neural Networks (RNN) and Deep Neural Networks (DNN), two pivotal AI technologies. You’ll start with the basics of RNNs and their applications, followed by an examination of DNNs, including their architecture and implementation using PyTorch. You will master building and deploying sophisticated AI models, develop RNN models for tasks like speech recognition and machine translation, understand and implement DNN architectures, and utilize PyTorch for model building and optimization. By the end, you'll have a robust knowledge of RNNs and DNNs and the confidence to apply these techniques in real-world scenarios. Designed for data scientists, machine learning engineers, and AI enthusiasts with basic programming (preferably Python) and statistics knowledge, this course combines theory with practical application through video lectures, hands-on exercises, and real-world examples.

• Introduction
• In this module, we will introduce you to the course instructor, providing insights into their background and expertise. Additionally, we will outline the primary focus and objectives of the course, setting the stage for your learning journey in AI sciences.
• Applications of RNN
• In this module, we will delve into the diverse applications of Recurrent Neural Networks (RNNs). You will learn to recognize human activities in videos, generate image captions, perform machine translation, and implement speech recognition. We will also explore using RNNs for stock price predictions and determine appropriate scenarios for modeling RNNs.
• Deep Neural Network (DNN) Overview
• In this module, we will explore the fundamentals of Deep Neural Networks (DNNs) and their implementation using PyTorch. You will learn about the architecture and representational power of DNNs, understand the importance of activation functions, and get hands-on experience with perceptrons. We will also cover gradient descent techniques, loss functions, and optimization strategies for building and refining DNN models.