Neural networks are a fundamental concept in artificial intelligence. They mimic the structure and function of the human brain, allowing machines to learn from data and make decisions.

Key Components of Neural Networks

  • Neurons: The basic building blocks of a neural network, which perform calculations and transmit signals.
  • Layers: A neural network consists of layers of neurons, including input, hidden, and output layers.
  • Weights and Biases: Adjusted during the training process to improve the accuracy of predictions.

Types of Neural Networks

  • Feedforward Neural Networks: Simplest type of neural network, where the data moves in only one direction.
  • Convolutional Neural Networks (CNNs): Used primarily for image recognition and classification.
  • Recurrent Neural Networks (RNNs): Ideal for processing sequential data, such as time series or natural language.

Applications of Neural Networks

  • Image and Video Recognition: Used in self-driving cars, medical diagnosis, and security systems.
  • Natural Language Processing (NLP): Applications include chatbots, language translation, and sentiment analysis.
  • Speech Recognition: Used in voice assistants, transcription services, and speech-to-text applications.

Convolutional Neural Network

For more information on neural networks and their applications, check out our Deep Learning course.