Welcome to this introductory guide to Neural Networks. We'll explore the core concepts and illustrate them with simple examples.
Neural Networks are inspired by the human brain. They consist of interconnected nodes, called neurons, that learn by adjusting the strength of the connections between them.
Let's consider a simple example: Imagine a network of nodes that receive input, process it, and output a result. Each connection has a weight associated with it.
The network learns by adjusting these weights through a process called training. We feed it data, and it makes predictions. Then, we compare the prediction to the correct answer and adjust the weights to improve the prediction accuracy.
This is a simplified example to demonstrate the fundamental concept. Real neural networks are incredibly complex and involve sophisticated algorithms.
This is just a starting point for understanding neural networks. There are many advanced concepts to explore.