A neural network is a type of machine learning model inspired by the human brain. It consists of layers of artificial neurons that process and learn patterns from data. Neural networks are widely used in image recognition, natural language processing, speech recognition, and many other AI applications.
Basic Structure of a Neural Network:
1. Input Layer – Receives data (e.g., images, text, or numbers).
2. Hidden Layers – Process data through weighted connections and activation functions.
3. Output Layer – Produces the final prediction or classification.
Types of Neural Networks:
1. Feedforward Neural Networks (FNN) – Data moves in one direction; used in simple tasks.
2. Convolutional Neural Networks (CNN) – Used in image processing and computer vision.
3. Recurrent Neural Networks (RNN) – Used in sequential data like speech and text.
4. Transformers – Advanced models for NLP (e.g., GPT, BERT).
5. Generative Adversarial Networks (GANs) – Used for generating realistic images and content.
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