In this episode, we dive deep into the world of Recurrent Neural Networks (RNNs)—the foundational architecture that gave machines the ability to understand sequences and context. You’ll learn how RNNs work, why they matter, and how they’re used in real-world applications like voice assistants, language translation, music generation, and time-series forecasting. We’ll break down key concepts like hidden states, vanishing gradients, and how LSTMs and GRUs evolved to solve RNN limitations. Whether you’re new to AI or brushing up on your deep learning knowledge, this episode will help you understand how machines learn from the past to predict the future.