Different Brains for Different Jobs
Just like we use different parts of our brain for different tasks — talking, seeing, remembering — AI also has different types of brains called neural networks.
Each one is good at a specific kind of work!
1️⃣ Feed-Forward Neural Network (FFNN)
A Feed-Forward Neural Network is the simplest type of neural network.
In this network, information flows only in one direction — from the input layer, through one or more hidden layers, and finally to the output layer.
There is no feedback or looping; data never moves backward.
Each neuron takes inputs, processes them, and passes the result to the next layer.
It does not remember previous data, which means it’s best for problems where each input is independent of the others.
Example:
Predicting exam marks from “hours studied” and “attendance.”
Each input is separate; the network doesn’t need memory of previous examples.
Advantages:
- Simple and easy to design
- Fast to train and run
- Works well for static data
Limitations:
- Cannot handle time-based or sequential data
- Has no memory of past events
2️⃣ Convolutional Neural Network (CNN)
A Convolutional Neural Network is a special kind of neural network designed mainly for image and video recognition.
It works like the human eye — detecting edges, colors, and shapes, and combining them to recognize complete objects.
CNNs use three main types of layers:
- Convolutional Layer: Detects important visual features like edges or corners
- Pooling Layer: Reduces the size of data to make computation faster
- Fully Connected Layer: Makes the final decision (e.g., “This is a dog”)
Example:
- Face recognition on phones
- Detecting traffic signs in self-driving cars
- Medical image analysis (like detecting tumors in scans)
Advantages:
- Automatically extracts features from images
- Very accurate for visual tasks
- Reduces manual feature engineering
Limitations:
- Needs a lot of data to train
- High computational cost (needs GPUs)
3️⃣ Recurrent Neural Network (RNN)
A Recurrent Neural Network is designed for sequential data — where the order of information matters.
It has loops in its connections, which allow it to “remember” what happened earlier.
This memory makes it ideal for data that comes in a sequence, such as speech, text, or time-series data.
Unlike FFNN, which only looks at the current input, RNN also considers previous inputs while making decisions.
Example:
- Predicting the next word in a sentence
- Translating one language to another
- Forecasting stock prices or weather trends
Advantages:
- Has memory of past information
- Works well for text, speech, and time-based data
Limitations:
- Slow to train
- May forget older information (solved by advanced RNNs like LSTM or GRU)
Comparison Table
| Type | Direction of Data | Has Memory | Best For | Example |
| Feed-Forward NN | One-way (input → output) | No | Simple predictions | Predicting marks |
| CNN | One-way (image features) | No | Image & video tasks | Face recognition |
| RNN | Loops back (remembers past) | Yes | Sequence & time data | Text / Speech |
When to Use Which Network
| Network Type | When to Use It | Example Applications |
| Feed-Forward NN | When input and output are simple and independent of each other | Predicting exam marks, credit score prediction |
| Convolutional NN (CNN) | When you’re working with images, videos, or visual data | Face detection, traffic sign recognition, medical image analysis |
| Recurrent NN (RNN) | When data has a sequence or depends on past information | Text generation, speech recognition, time-series forecasting |
In Simple Words:
- Feed-Forward Network: Thinks in a straight line — best for basic predictions.
- CNN: Sees the world — best for images and visuals.
- RNN: Remembers the story — best for text, speech, and sequences.