LSTM vs GRU: Three Gates vs Two Gates in Recurrent Memory

Mon, 03 Aug 2026 03:33:36 +0900

Overview

LSTM and GRU are both gated recurrent architectures built to capture long-range dependencies in sequences while avoiding the vanishing-gradient problem of vanilla RNNs. LSTM keeps a dedicated cell state alongside its hidden state, regulated by three gates, while GRU folds everything into a single hidden state updated by just two gates. That structural difference drives everything else: parameter count, training speed, and how precisely you can control what the network remembers.

CNN vs RNN: Spatial Convolution vs Sequential Recurrence

Mon, 03 Aug 2026 03:31:06 +0900

Overview

Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are neural architectures built for different data shapes: CNNs slide convolutional filters across a spatial grid to detect local patterns, while RNNs pass a recurrent hidden state across time steps to model sequential dependencies. Choosing between them (or their modern successors) depends on whether your data’s structure is spatial, temporal, or both.

Comparison Diagram

Comparison Table

Aspect	CNN	RNN
Input data shape	Fixed-size spatial grid (2D/3D tensors like images)	Variable-length ordered sequence (text, time series, audio)
Core operation	Convolution: a filter slides over local receptive fields	Recurrence: hidden state updated step-by-step from previous state plus current input
Weight sharing	Same filter weights reused across all spatial positions	Same weight matrices reused across all time steps
Context captured	Local spatial neighborhoods, expanded via depth/pooling	Temporal history accumulated in the hidden state over prior steps
Order sensitivity	Largely order-invariant beyond local structure; pooling discards exact position	Strictly order-dependent; reordering the sequence changes the output
Training parallelization	Highly parallelizable across positions, channels, and layers	Inherently sequential; each step waits on the previous hidden state
Common failure mode	Limited receptive field unless network is deep or uses dilation	Vanishing/exploding gradients over long sequences
Typical applications	Image classification, object detection, segmentation	Language modeling, time-series forecasting, speech recognition

Key Differences

CNNs assume spatially local structure and share filter weights across the whole input; RNNs share weights across time steps instead.
CNN layers process all positions in parallel, while RNNs are sequential by construction since each step needs the prior hidden state.
RNNs suffer from vanishing gradients over long sequences; CNNs sidestep this but need deeper stacks to grow their receptive field.
Shuffling pixels barely changes what a CNN detects, but reordering a sequence fed to an RNN changes the output entirely, since RNNs are order-sensitive.
CNNs expect fixed-size grid inputs, whereas RNNs natively handle variable-length sequences.

When to Use Each

CNN

Rnn on IT Comparison

LSTM vs GRU: Three Gates vs Two Gates in Recurrent Memory

Overview

CNN vs RNN: Spatial Convolution vs Sequential Recurrence

Overview

Comparison Diagram

Comparison Table

Key Differences

When to Use Each