Neural Networks - Introduction

Neural networks are computational models inspired by the human brain.
They are widely used in Machine Learning and Deep Learning.

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📌 What is a Neural Network?

A neural network is composed of:

• Input layer
• Hidden layers
• Output layer
• Weights
• Activation functions

A neural network learns by adjusting its weights to minimise a loss function.

🔍 Basic Structure

Component Description Example

Input Layer Receives raw data Images, Text Hidden Layer Performs transformations Dense Layer Output Layer Produces prediction Class label

⚙️ Training Process

1. Forward propagation
2. Compute loss
3. Backpropagation
4. Update weights

🧮 Example Formula

y = activation(Wx + b)

Where:

• W = weights matrix\
• x = input vector\
• b = bias\
• activation() = non-linear function

🔥 Types of Neural Networks

• Feedforward Neural Networks
• Convolutional Neural Networks (CNN)
• Recurrent Neural Networks (RNN)
• Transformers

📊 Advantages vs Limitations

Advantages Limitations

High accuracy Requires large datasets Learns complex patterns Computationally expensive Works well for images/text Hard to interpret

🚀 Conclusion

Neural networks are at the core of modern AI systems and are used in:

• Computer Vision
• Natural Language Processing
• Speech Recognition
• Autonomous Systems

End of Article

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