En bref The following article plunges into the power and limits of recurrent neural networks, outlining how researchers and engineers harness memory, gating, and backpropagation through time to extract meaningful signals from sequential data. We explore foundational architectures, practical deployment considerations on modern ML stacks, real-world applications across industries, and the evolving landscape where RNNs …