☆ Save Deep Learning Architectures — CNN vs RNN vs Transformer (Complete Guide)

04/17/2026

If you want to understand the structural differences among deep learning architectures such as DNN·CNN·RNN·and Transformer at a glance, this is the right place to start.

• This hub acts as a fast navigation map that connects the full flow from the DNN starting point → architecture branching by data type → the evolution of major vision models → the expansion of sequence models → the shift to Transformers.
• You can immediately choose the single best starting article and the shortest learning path that matches your goal.

Main Starting Point — Begin with the one article that frames the architecture landscape

If you first want to understand where deep learning architectures began and why they later split into multiple families, this is the fastest article to open first.

• Deep Neural Network (DNN) — the starting point of deep learning architecture, where multiple hidden layers learn increasingly complex representations

If you want a clear baseline for how the overall architecture landscape branched out, start with this article first.

Quick Start — Pick your path right now

• Complete beginner — DNN → CNN → RNN
• Structure comparison first — CNN ↔ RNN → Transformer
• Representative model evolution — LeNet → AlexNet → ResNet
• Up to the modern paradigm — Attention → Transformer

1. The Starting Point — Where do deep learning architectures begin?

The origin of deep learning architectures is straightforward. They begin with structures that stack more layers to learn more complex representations. Once you understand this baseline, it becomes much easier to see why CNNs, RNNs, and Transformers emerged later.

• Deep Neural Network (DNN) — the fundamental architecture whose representational power grows as depth increases

If this is your first pass through the topic, start here so that the later architectures connect naturally.

2. Architectural Branching — How do models diverge depending on the data?

Deep learning does not process every kind of data in the same way. Core architectures diverge depending on the structure of the data.

• CNN — the standard architecture for images and spatial structure
• RNN — an architecture designed for sequential data and temporal flow
• Deep RNN — an extended recurrent structure for handling more complex temporal dependencies

If the difference between CNNs and RNNs still feels fuzzy, this is the section where you should clear that up first.

3. Vision Architecture Flow — How did CNN-based models evolve?

The evolution of deep learning architectures is especially visible in vision models. CNN-based systems, in particular, progressed through a series of major architectural innovations.

• Convolution Kernel — the filter used to extract features from the input
• Deep CNN — an architecture that learns higher-level features through deeper layers
• LeNet — the starting point of early CNNs
• AlexNet — the turning point that ignited the deep learning boom
• VGGNet — a model built by stacking a simple architecture very deep
• GoogLeNet (Inception) — a model that improved efficiency through parallel structure
• ResNet — a model that enabled the training of very deep networks through residual connections

If you want to grasp the full progression of representative vision models in one pass, start with LeNet and continue through ResNet.

4. Sequence Model Flow — Why did the RNN family become necessary?

When the data is not an image but a sequence where order and time matter, a different architecture becomes necessary. That is why the RNN family emerged, and later the center of gravity shifted toward Attention and Transformers.

• RNN — the basic architecture for sequence modeling
• Deep RNN — a deeper extension of recurrent architecture
• Attention Mechanism — a key structure that selects which information matters most

If you want to understand sequence-model evolution, the natural next step after RNN is Attention.

5. The Modern Shift — What became central after CNNs and RNNs?

After CNNs and RNNs, the center of deep learning shifted again. To understand the modern landscape, you need to understand the Transformer.

• Transformer — the next-generation architecture that became central to modern AI
• Representation Learning — a core topic for understanding how deep learning converts data into useful internal representations

If you want to continue all the way into the modern deep learning architecture landscape, move directly to Transformer from here.

Recommended Learning Paths — Continue based on your goal

• Start with architectural branching — DNN → CNN → RNN
• Focus on vision model evolution — LeNet → AlexNet → ResNet
• From sequence models to modern architectures — RNN → Attention → Transformer

Core Selection Guide

• Starting from scratch → Deep Neural Network (DNN)
• Comparing architectures → CNN / RNN
• Tracing representative model evolution → AlexNet → ResNet
• Understanding the modern architecture → Transformer

You only need to choose one next click from here. The point of this page is not to explain what deep learning is in the abstract, but to connect you directly to the structures that branched out and evolved over time.

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🔖 Tags: CNN · Deep Learning Architectures · RNN · sequence models · Transformer · vision models