Deep learning is a subset of machine learning that structures algorithms in layers to create an "artificial neural network" that can learn and make intelligent decisions on its own.

Key Components of Deep Learning

  1. Neural Networks: The building blocks of deep learning, consisting of layers of interconnected nodes or "neurons."
  2. Layers: Layers in a neural network can be input, hidden, or output layers, each performing different tasks in the learning process.
  3. Weights and Biases: Weights are the parameters that adjust the strength of connections between neurons, while biases are constants that shift the activation function output.

Deep Learning Applications

  • Image Recognition: Used in self-driving cars, medical imaging, and more.
  • Natural Language Processing (NLP): Powers language translation, sentiment analysis, and chatbots.
  • Speech Recognition: Enables voice assistants like Siri and Alexa.

Neural Network Diagram

For more in-depth information about deep learning, check out our comprehensive guide on Deep Learning Basics.