Basics of Neural Networks

Neural networks are a type of Artificial Intelligence (AI) that uses artificial neurons to learn and adapt. They are the foundation of many popular AI algorithms, such as ChatGPT, DeepSeek, Stable-Diffusion, etc.

A neural network is a type of Machine Learning model inspired by the structure and functioning of the human brain. It’s designed to recognize patterns, classify data, and make predictions. Neural networks consist of layers of interconnected nodes (also called neurons) that work together to process information.

Neural Network Components

Input layer: This is where the data enters the neural network. Each node in this layer represents a feature of the input data (e.g., pixels in an image, words in a sentence).

Hidden layers: These are layers between the input and output layers. Each node in a hidden layer performs a mathematical operation on the data it receives and passes the result to the next layer. The more hidden layers there are, the deeper the network, which can help the model learn more complex patterns.

Output layer: The final layer that produces the result, such as a classification label (e.g., “cat” or “dog“) or a continuous value (e.g., a predicted price).

 

 

 

Weights and biases: Each connection between nodes has a weight, which determines the strength of the signal passed between nodes. Each node also has a bias, which allows the model to make adjustments during training to improve its performance.

Activation functions: These are mathematical functions applied to the output of each node to introduce non-linearity. Without them, the network would only be able to learn linear relationships, which limits its ability to model complex data.

Neural networks “learn” by adjusting the weights and biases through a process called training. This usually involves using a large dataset and an optimization technique (like backpropagation) to minimize errors in predictions. The network gets better over time by making adjustments based on the error in its predictions.

Neural networks are used in various applications, including image recognition, natural language processing, speech recognition, game playing, etc.