recurrent neural networks (rnn) - explained #maths #dataanlysis #datascience #machinelearning

2026-03-01 · Source: DataMListic · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Intermediate, quick

Summary

Recurrent Neural Networks (RNNs) differ from feedforward networks by incorporating a memory mechanism. Unlike feedforward networks where inputs are processed and immediately forgotten, RNNs combine the current input with a hidden state from the previous step to generate a new hidden state. This new hidden state then loops back as an input to the subsequent step, effectively creating a compressed memory of all prior inputs. Inside a recurrent cell, the current input XRT is multiplied by a weight matrix WX, and the previous hidden state H bar one is multiplied by its own weight matrix WH. These transformed values are summed and then passed through a tanh activation function, which bounds the output between -1 and 1, producing the new hidden state h of t. This recurrence can be unrolled into a chain of identical cells, all sharing the same weights and parameters across different time steps.

Key takeaway

For Machine Learning Engineers working with sequential data like time series or natural language, understanding RNNs is crucial. Your models can capture temporal dependencies by leveraging the hidden state as a memory, allowing for more context-aware predictions. Consider implementing a basic RNN to see how its recurrent nature processes information over time, especially when traditional feedforward networks fall short on tasks requiring memory.

Key insights

RNNs use a hidden state as a compressed memory, combining current input with past information.

Principles

• RNNs maintain memory via a hidden state.
• Weights are shared across time steps in RNNs.

Method

The recurrent cell calculates a new hidden state h_t = tanh(W_H * h_{t-1} + W_X * x_t), where h_{t-1} is the previous hidden state and x_t is the current input.

In practice

• Model sequential data with RNNs.
• Use shared weights for temporal consistency.

Topics

• Recurrent Neural Networks
• Neural Network Memory
• Hidden State
• Weight Matrices
• Tanh Activation Function

Best for: AI Student, Machine Learning Engineer, Data Scientist

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• Memory Caching: RNNs with Growing Memory — Memory Caching enhances RNNs by enabling memory growth with sequence length, bridging the performance gap with Transformers.
• DIY #21 - Step-by-Step Guide to Time Series Forecasting with RNN — Vanilla RNNs effectively forecast cyclical time series data by learning temporal patterns through sequential processing.
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• Pretraining Recurrent Networks without Recurrence — SMT enables parallel RNN training and stable long-range dependency learning by decoupling memory updates from credit propagation.

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Editorial summary, takeaway, and curation by AIssential. Original article published by DataMListic.