1️⃣ Generative Adversarial Network (GAN)
👉 What it is (simple words)
A GAN is a system of two neural networks competing against each other to generate fake data that looks real.
Think of it like:
- Generator = a counterfeiter
- Discriminator = a police officer
They train together until the counterfeiter becomes extremely good.
🏗️ Internal Structure
GAN has two networks:
- Generator
- Takes random noise
- Produces fake images, text, audio, etc.
- Discriminator
- Takes real + fake data
- Decides: real or fake?
They play a zero-sum game.
🔁 Training Logic
- Generator creates fake data
- Discriminator checks it
- Discriminator gives feedback
- Generator improves
- Loop until fake looks real
✅ Where GANs are used
- Image generation (faces, art)
- Image enhancement (super-resolution)
- Deepfakes
- Data augmentation
- Style transfer
⚠️ Weaknesses
- Hard to train
- Can collapse (mode collapse)
- No clear accuracy metric
2️⃣ Recurrent Neural Network (RNN)
👉 What it is (simple words)
An RNN is designed to remember past information.
It processes sequences step by step and carries memory forward.
Think:
- Sentence reading
- Time-series prediction
- Speech recognition
🏗️ Internal Structure
- Input at time
t - Hidden state (memory)
- Output at time
t - Hidden state passes to time
t+1
Same network reused at every step.
🔁 How it works
Example: Sentence
I → love → AWS
- When reading "AWS", the model still remembers "I love"
✅ Where RNNs are used
- Language translation
- Chatbots (older ones)
- Speech recognition
- Stock price prediction
- Log analysis
⚠️ Weaknesses
- Struggles with long sequences
- Vanishing gradient problem
- Slower training
(Reason why LSTM and GRU were invented)
3️⃣ Convolutional Neural Network (CNN)
👉 What it is (simple words)
A CNN is specialized for images.
It learns by detecting patterns like edges, shapes, textures, then combines them into objects.
🏗️ Internal Structure
- Convolution layers
- Apply filters
- Detect features
- Pooling layers
- Reduce size
- Keep important info
- Fully connected layers
- Final decision
🔁 How it works
Image →
- Detect edges →
- Detect shapes →
- Detect objects →
- Classification
✅ Where CNNs are used
- Image classification
- Face recognition
- Object detection
- Medical imaging
- Video analysis
⚠️ Weaknesses
- Needs lots of data
- Computationally heavy
- Not good for sequences
🔥 Side-by-Side Comparison (IMPORTANT)
🆚 GAN vs RNN vs CNN
| Feature | GAN | RNN | CNN |
|---|---|---|---|
| Primary Purpose | Generate new data | Process sequences | Process images |
| Key Idea | Competition (Generator vs Discriminator) | Memory over time | Spatial feature extraction |
| Input Type | Random noise + real data | Sequential data | Grid-like data (images) |
| Output | New synthetic data | Prediction per step or sequence | Classification / detection |
| Memory | ❌ No | ✅ Yes | ❌ No |
| Training Style | Adversarial | Sequential backprop | Feedforward |
| Hard to Train | ✅ Very | ⚠️ Medium | ❌ Easier |
| Typical Use Case | Image generation | Language, time-series | Computer vision |
🧠 One-Line Memory Trick
- GAN = Create fake data
- RNN = Remember the past
- CNN = See patterns in images
🎯 Selection Guide
| Question Mentions | Choose |
|---|---|
| Generate images, fake data, deepfakes | GAN |
| Time-series, text, speech, logs | RNN |
| Images, vision, object detection | CNN |
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