What is a Neural Network?
The Brain Behind Artificial Intelligence — Explained by The Priyanuj Hazarika
🧠 Introduction
A Neural Network is a computational system inspired by how the human brain works. It consists of layers of interconnected nodes (neurons) that process data, recognize patterns, and make smart predictions — similar to how our brain processes information.
⚙️ How Does a Neural Network Work?
Neural networks are structured in three main layers:
- Input Layer: Accepts data such as images, numbers, or text.
- Hidden Layers: Processes data using mathematical operations and activation functions.
- Output Layer: Produces the final decision or classification.
🔍 Types of Neural Networks
- Feedforward Neural Network (FNN): The simplest form where data flows in one direction.
- Convolutional Neural Network (CNN): Used for image and video recognition.
- Recurrent Neural Network (RNN): Best for sequential data like text and speech.
- Generative Adversarial Network (GAN): Creates AI-generated images, videos, and even music.
🌍 Real-World Applications
- Facial recognition in smartphones
- Autonomous driving (Tesla, Waymo)
- AI-based medical diagnosis
- Stock market prediction models
- Chatbots and voice assistants
💡 Why Neural Networks Matter
Neural Networks are the heart of Artificial Intelligence (AI) and Deep Learning. They help machines learn, think, and adapt on their own — powering a smarter digital future.
📘 Conclusion
Neural Networks represent a major step toward true machine intelligence. As data grows and technology evolves, they’ll continue to revolutionize how we work, communicate, and live — shaping the future of AI, all explained by The Priyanuj Hazarika.
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