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 Concepts

  • Neural Networks: Inspired by the human brain, neural networks are composed of interconnected nodes or "neurons" that work together to process information.
  • Layers: Deep learning models have multiple layers, including input, hidden, and output layers. Each layer performs a specific task in the learning process.
  • Backpropagation: This is a method used to train neural networks by adjusting the weights of the neurons based on the error rate.

Types of Deep Learning Models

  • Convolutional Neural Networks (CNNs): Great for image recognition and classification.
  • Recurrent Neural Networks (RNNs): Suited for sequential data like time series or text.
  • Generative Adversarial Networks (GANs): Used for generating new data with similar statistics to real-world data.

Resources

For more in-depth learning about deep learning, check out our Deep Learning Tutorial.

Deep Learning Neural Network