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What is a Neural Network

A neural network is a type of machine learning algorithm inspired by the human brain. It's a powerful tool that excels at solving complex problems more difficult for traditional computer algorithms to handle, such as image recognition and natural language processing (NLP)

Neural Networks – Summary

A neural network is a brain-inspired AI model that learns patterns from data using interconnected neurons organized into layers. Instead of following fixed rules, it improves performance by adjusting connection strengths (weights) during training, making it effective for complex tasks such as image recognition and language understanding.

Neural networks are a core part of machine learning, enabling systems to learn directly from examples and handle problems that are difficult to solve with traditional programming.

Key Components

  • Neurons (Nodes): Basic units that receive, process, and transmit information.
  • Layers:
    • Input Layer: Accepts raw data such as images, text, or audio.
    • Hidden Layers: Perform intermediate processing and feature extraction.
    • Output Layer: Produces the final prediction or result.
  • Weights & Biases: Adjustable numerical values that control signal strength and learning accuracy.

Neural network components

Test thử

How It Works

  1. Input: Data enters through the input layer.
  2. Processing: Hidden layers transform data using weights and activation functions.
  3. Output: The model generates a prediction.
  4. Learning: Errors are calculated and weights are updated using backpropagation to improve future results.

Applications

  • Computer Vision: Image recognition, self-driving cars
  • Natural Language Processing (NLP): Chatbots, translation, summarization
  • Speech Recognition: Voice assistants
  • Healthcare: Medical diagnosis and analysis