Neural Networks - Introduction

🧠 Understanding Neural Networks

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

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Component Description Example

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

⚙️ Training Process

Forward propagation
Compute loss
Backpropagation
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

Neural networks have revolutionised artificial intelligence by enabling models to learn hierarchical representations of data.
However, they require careful tuning, significant computational power, and high-quality datasets.

🚀 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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echo "Guilhem, Ralph, reveillez-vous"

int ft_main(int test, int test2) {
	int a = 0 
	int b = 0
	int result = 0
	
	result = a + b
	
	return result

}