Fundamentals of Simple Recurrent Neural Networks

Sequential data is all around us, from natural language to time-series measurements, yet standard neural networks struggle to process it. This course introduces you to the core architecture designed specifically for sequential patterns: the Recurrent Neural Network (RNN). You will transition from understanding static data flows to grasping how neural networks maintain memory across time steps. By the end of this course, you will understand how information flows through an RNN cell, how recurrent connections maintain state, and how these networks learn. What you will learn: Understand the foundational architecture of a simple Recurrent Neural Network cell; Learn how hidden states carry memory forward across multiple time steps; Practice calculating forward propagation step-by-step through mathematical explanations; Master the concept of backpropagation through time (BPTT) and how gradients flow; Identify the limitations of basic RNNs, including vanishing and exploding gradients; Explore modern sequential modeling concepts, including a conceptual introduction to gated architectures and attention mechanisms. You will begin by exploring the absolute basics of sequential data and key terminology, before breaking down the mathematical operations inside a single RNN cell. The course then guides you through the training process and the challenges of long-term memory in deep learning. This course is designed for beginners in deep learning, software developers, and data enthusiasts looking to understand sequential models without needing advanced mathematical prerequisites. Start reading today to unlock the mechanics of sequential neural networks.