A Neural Network is a computational model inspired by how the human brain works. It consists of layers of connected nodes called neurons that process information and learn patterns from data.
Neural networks are the backbone of Deep Learning and power many modern AI applications.
How Neural Networks Work
A neural network has three main layers:
1. Input Layer
Receives the raw data (images, text, numbers, etc.).
2. Hidden Layers
Process the data through mathematical operations to detect patterns.
3. Output Layer
Produces the final prediction or classification.
Each connection between neurons has a weight, and learning happens when these weights adjust over time.
Activation Functions
Activation functions decide whether a neuron should “fire.”
Common ones include:
- ReLU
- Sigmoid
- Tanh
- Softmax
These functions introduce non-linearity, allowing networks to learn complex patterns.
Types of Neural Networks
- Feedforward Neural Network (FNN)
- Convolutional Neural Network (CNN)
- Recurrent Neural Network (RNN)
- Transformers
- Autoencoders
Different types are used for images, text, sequences, and more.
Real-World Applications
Neural networks are used in:
- Image recognition
- Speech-to-text systems
- Chatbots and language models
- Medical diagnosis
- Fraud detection
- Translation apps
They make AI capable of performing human-like tasks accurately.
Conclusion
Neural networks allow machines to learn complex patterns from data and make smart decisions. Understanding them is essential for mastering AI and deep learning.
Citations
https://savanka.com/category/learn/ai-and-ml/
https://www.w3schools.com/ai/