Neural Networks Explained Simply

December 2025

Let’s break down all these neural networks in everyday language, as if explaining to someone without a technical background.

The Basics: What's a Neural Network?

Think of a neural network like a student learning to recognize patterns. Just as you learned to identify cats by seeing many examples, neural networks learn from examples too. They're made of "neurons" (simple calculators) connected, and they adjust their connections to get better at their task.

Feedforward Networks - The Simplest Learners

Multilayer Perceptron (MLP)

• What it does: The basic pattern recognizer
• How it works: Information flows straight through from input to output, like an assembly line
• Real-world example: Deciding if an email is spam based on words it contains, or predicting house prices based on size and location

Radial Basis Function Network (RBF)

• What it does: Recognizes patterns by measuring "how close" something is to examples it knows
• How it works: Like a store clerk who remembers regular customers - the closer you look to someone they know, the better they recognize you
• Real-world example: Predicting tomorrow's temperature based on similar weather patterns from the past

Convolutional Neural Networks (CNNs) - The Vision Experts

Standard CNN

• What it does: Understands images by looking for patterns like edges, shapes, and textures
• How it works: Like scanning a photo with a magnifying glass, looking at small pieces at a time, then combining what it finds
• Real-world example: Face recognition on your phone, identifying tumors in medical scans, self-driving cars recognizing stop signs

ResNet (Residual Networks)

• What it does: A very deep CNN that can learn extremely complex patterns
• How it works: Has "shortcuts" that help information flow through many layers without getting lost, like having express lanes on a highway
• Real-world example: Google Photos organizing thousands of pictures, advanced medical diagnosis

U-Net

• What it does: Outlines and identifies specific regions in images
• How it works: First shrinks the image to understand the big picture, then expands it back while drawing precise boundaries
• Real-world example: Outlining organs in medical scans, identifying buildings in satellite images, tracking cells under microscopes

Inception Networks

• What it does: Looks at images at multiple scales simultaneously
• How it works: Like having multiple people examine a painting - one looks at brushstrokes, another at overall composition, another at colors
• Real-world example: Detecting objects of different sizes in one photo (finding both a person and a distant car)

Recurrent Neural Networks (RNNs) - The Memory Keepers

Vanilla RNN

• What it does: Processes sequences by remembering what it just saw
• How it works: Like reading a sentence word by word, keeping track of what came before
• Real-world example: Predicting the next word while typing on your phone
• Limitation: Has trouble remembering things from long ago (like forgetting the beginning of a long sentence)

LSTM (Long Short-Term Memory)

• What it does: Remembers important information over long sequences
• How it works: Has special "gates" that decide what to remember, what to forget, and what to pay attention to - like your brain deciding which details from a story matter
• Real-world example: Language translation, voice assistants understanding long sentences, predicting stock prices based on historical trends, autocomplete that remembers context

GRU (Gated Recurrent Unit)

• What it does: A simpler, faster version of LSTM
• How it works: Similar to LSTM but with fewer moving parts, like a simpler lock mechanism
• Real-world example: Same as LSTM but when speed matters more than handling extremely long sequences

Bidirectional RNN/LSTM

• What it does: Reads sequences forward AND backward
• How it works: Like reading a mystery novel twice - once normally, once knowing the ending - to understand everything better
• Real-world example: Understanding speech (where future words help clarify earlier ones), analyzing DNA sequences

Transformers - The Attention Masters

Transformer

• What it does: Processes entire sequences at once by focusing on relevant parts
• How it works: Like a speed reader who can look at a whole paragraph and immediately focus on the important words, without reading linearly
• Real-world example: Google Translate, understanding long documents

BERT

• What it does: Deeply understands language by reading in both directions
• How it works: Trained by hiding random words and learning to guess them from context - like doing crossword puzzles to learn language
• Real-world example: Google Search understanding your questions, chatbots understanding intent, analyzing customer reviews

GPT (Generative Pre-trained Transformer)

• What it does: Generates human-like text by predicting what comes next
• How it works: Trained on massive amounts of text to predict the next word, over and over, learning patterns of language
• Real-world example: ChatGPT, writing assistance, code completion, creative writing

Vision Transformer (ViT)

• What it does: Applies the transformer approach to images
• How it works: Breaks images into patches and treats them like words in a sentence
• Real-world example: Image classification competing with traditional CNNs

Generative Networks - The Creators

GAN (Generative Adversarial Network)

• What it does: Creates realistic fake data
• How it works: Two networks compete - one tries to create fakes, the other tries to spot them. Like an art forger versus an art detective, making each other better
• Real-world example: Creating realistic faces that don't exist, generating artwork, improving old photo quality, deepfakes

VAE (Variational Autoencoder)

• What it does: Learns to create variations of things
• How it works: Compresses data into a "recipe," then generates new examples by tweaking the recipe slightly
• Real-world example: Generating new drug molecules, creating variations of designs, finding anomalies in manufacturing

Diffusion Models

• What it does: Creates images by gradually removing noise
• How it works: Like a sculptor revealing a statue by gradually removing marble - starts with pure noise and slowly refines it into an image
• Real-world example: DALL-E, Midjourney, Stable Diffusion - creating images from text descriptions

Autoencoders - The Compressors

Standard Autoencoder

• What it does: Learns to compress and decompress data
• How it works: Like packing a suitcase efficiently - it learns what's essential and what can be left out
• Real-world example: Compressing images, removing noise from photos, finding defects in manufacturing

Denoising Autoencoder

• What it does: Learns to clean up messy data
• How it works: Trained on deliberately corrupted data to learn what "clean" looks like
• Real-world example: Restoring old photographs, cleaning up audio recordings

Sparse Autoencoder

• What it does: Finds the minimal essential features
• How it works: Forces itself to use very few neurons, discovering only the most important patterns
• Real-world example: Finding key features in data for analysis, dimensionality reduction

Graph Neural Networks - The Relationship Analyzers

Graph Convolutional Network (GCN)

• What it does: Learns from connected data (networks)
• How it works: Like understanding a social network by looking at who's friends with whom and what they share in common
• Real-world example: Friend recommendations on Facebook, analyzing molecular structures, detecting fraud in transaction networks, recommendation systems ("customers who bought this also bought...")

Graph Attention Network (GAT)

• What it does: GCN but focuses on the most important connections
• How it works: Like knowing which friendships in your social circle matter most for different situations
• Real-world example: Better social network analysis, knowledge graphs

Specialized Architectures

Siamese Networks

• What it does: Compares two things to see how similar they are
• How it works: Two identical networks process two inputs, then compare their outputs - like having twins examine two items independently and report similarities
• Real-world example: Face verification ("Is this the same person?"), signature verification, finding similar products

Capsule Networks

• What it does: Understands objects and their spatial relationships better
• How it works: Groups neurons into "capsules" that represent parts of objects and their positions - like understanding that a face has eyes, nose, mouth in specific arrangements
• Real-world example: Recognizing objects from different angles, better image understanding

Memory Networks

• What it does: Has an external memory bank to store and retrieve information
• How it works: Like a person who takes notes and refers back to them while thinking
• Real-world example: Answering questions that require looking up multiple facts, complex reasoning tasks

Echo State Networks

• What it does: Fast way to process sequences
• How it works: Has a complex, fixed "reservoir" of neurons that creates rich patterns, with only the output needing training
• Real-world example: Real-time prediction, control systems

Self-Organizing Maps

• What it does: Creates maps of data showing what's similar
• How it works: Like organizing a messy desk by grouping similar items together on a 2D surface
• Real-world example: Visualizing customer segments, organizing documents by topic

Hopfield Networks

• What it does: Stores and retrieves patterns like memory
• How it works: Like remembering a song from just a few notes - given part of a pattern, it recalls the whole thing
• Real-world example: Pattern completion, associative memory, optimization

Boltzmann Machines / RBM

• What it does: Learns probability distributions of data
• How it works: Uses probability and energy concepts, like water finding the lowest level
• Real-world example: Recommendation systems (Netflix recommendations), learning features from data

Physics & Science-Informed Networks

Physics-Informed Neural Networks (PINN)

• What it does: Solves physics problems while respecting physical laws
• How it works: Like a student who not only learns from examples but also follows the physics equations they learned in class
• Why it's special: Can work with less data because it knows the rules of physics
• Real-world example: Predicting fluid flow, heat distribution, structural stress, weather patterns - all while obeying laws of physics

Theory-Informed Neural Networks (general TINN)

• What it does: Like PINN but follows any domain rules, not just physics
• How it works: Incorporates expert knowledge and theoretical constraints into learning
• Real-world example: Epidemic modeling (following disease spread rules), economic forecasting (following economic theories), ecology

Thermodynamics-Informed Neural Networks (specific TINN)

• What it does: Ensures predictions follow thermodynamics laws (energy conservation, entropy)
• How it works: Like a chef who not only learns recipes but also knows heat flows from hot to cold and energy is conserved
• Why it's critical: Prevents impossible predictions (like a battery that never runs out)
• Real-world example: Battery management, chemical reactions, material phase changes, combustion engines

DeepONet

• What it does: Learns how one function transforms into another
• How it works: Like learning the general rule of "if you do this operation, this happens" rather than memorizing specific cases
• Real-world example: Solving many related physics problems at once

Fourier Neural Operators

• What it does: Solves physics equations super fast using frequency analysis
• How it works: Works in "frequency space" like analyzing sound waves by their pitches
• Real-world example: Fast weather prediction, turbulence modeling

Neural ODEs

• What it does: Models continuously changing systems
• How it works: Instead of discrete steps, treats change as continuous flow - like watching a movie instead of flipping through photos
• Real-world example: Modeling growth, chemical reactions, any continuously changing process

Hamiltonian/Lagrangian Neural Networks

• What it does: Learns physics that conserves energy
• How it works: Bakes in the mathematics of energy conservation, like a pendulum that doesn't lose energy
• Real-world example: Robotics, planetary motion, molecular simulations

Why So Many Types?

Different problems need different solutions:

• Images? Use CNNs
• Text or sequences? Use RNNs, LSTMs, or Transformers
• Creating new content? Use GANs, VAEs, or Diffusion Models
• Network data? Use Graph Neural Networks
• Physics problems? Use Physics-Informed Networks
• Need to obey energy laws? Use Thermodynamics-Informed Networks

Each architecture evolved to solve specific challenges, like having different tools in a toolbox - you wouldn't use a hammer for everything!

 

We typically call them "models" in everyday conversation, though technically they involve both models and algorithms working together. Here's why:

Think of it Like Cooking

Imagine you want to make a cake:

The Recipe (Algorithm)

• This is the step-by-step instructions: "Mix flour and sugar, add eggs, bake at 350°F for 30 minutes"
• It's the process or method you follow
• It tells you HOW to do something

The Actual Cake (Model)

• This is the finished product you can use
• It's the result of following the recipe
• It's the thing you can now serve to people

Applied to Neural Networks

Algorithm = The Training Recipe

• Backpropagation is an algorithm (the method for teaching the network)
• Gradient descent is an algorithm (the way it improves)
• These are the instructions for HOW to train

Model = The Trained Neural Network

• A CNN that recognizes cats is a model
• GPT that generates text is a model
• It's the trained system you can use
• It's what you get after applying the algorithms

Why It Gets Confusing

People often use these terms loosely because:

1. "Neural network architecture" = The blueprint (like saying "a three-layer cake")
2. "Training algorithm" = The method to create it (the baking process)
3. "Trained model" = The final product you use (the finished cake)

When someone says, "I'm using a CNN model", they usually mean: "I have a trained Convolutional Neural Network that I can now use for predictions."

When someone says, The backpropagation algorithm", they mean: "The method used to train neural networks."

Real-World Analogy

Think about GPS navigation:

Algorithm (The Method)

• Dijkstra's algorithm (finds shortest path)
• The instructions the GPS follows to calculate routes
• The HOW: "Check all possible roads, compare distances, find shortest..."

Model (The Map/System)

• The actual GPS system with all the map data
• The tool you use in your car
• The WHAT: "This is the system that gives you directions"

You don't need to know the algorithm to use the GPS. You just use the model!

So What Should You Say?

Most of the time, say "model":

• "I'm using a BERT model for text analysis"
• "This GPT model generates responses"
• "A CNN model identified the image"

Say "algorithm" when talking about the method:

• "The model was trained using the Adam algorithm"
• "Backpropagation algorithm updates the weights"
• "The gradient descent algorithm minimizes error"

Use "architecture" for the structure:

• "The transformer architecture uses attention mechanisms"
• "ResNet architecture has skip connections"

The Technical Truth

A neural network is actually:

• An architecture (the design/blueprint)
• Trained by algorithms (the methods/recipes)
• Resulting in a model (the final trained system)

But in everyday speech, we just call the whole thing a "model" because that's what we interact with and use.

Bottom Line

Call them MODELS when talking about what they are and what they do:

• "A CNN model for image recognition"
• "An LSTM model for time series prediction"

Call them ALGORITHMS when talking about HOW they're trained or HOW they work internally:

• "Trained using the backpropagation algorithm"
• "Uses the attention algorithm"

Think of it this way:

• Algorithm = Recipe/Instructions
• Model = The finished dish you eat

You go to a restaurant and order a "dish" (model), not a "recipe" (algorithm), even though the recipe was used to make it!