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Deep Learning Models - JSON

Deep learning models are AI technologies that learn complex patterns using multi-layer neural networks. CNNs excel at image processing, RNNs at sequential data, Transformers at natural language processing, GANs at generative modeling, and Autoencoders at representation learning, forming the foundation of modern AI systems.

deep learning neural networks machine learning AI CNN RNN Transformer GAN Autoencoder

[
  {
    "code": "CNN",
    "slug": "convolutional-neural-network",
    "name": "Convolutional Neural Network",
    "description": "A neural network designed for processing spatial data such as images and video.",
    "category": "Image Processing"
  },
  {
    "code": "RNN",
    "slug": "recurrent-neural-network",
    "name": "Recurrent Neural Network",
    "description": "A neural network designed for processing sequential and temporal data.",
    "category": "Sequential Processing"
  },
  {
    "code": "Transformer",
    "slug": "transformer",
    "name": "Transformer",
    "description": "A neural network architecture using self-attention mechanism for parallel processing.",
    "category": "Natural Language Processing"
  },
  {
    "code": "GAN",
    "slug": "generative-adversarial-network",
    "name": "Generative Adversarial Network",
    "description": "A model where two networks (generator and discriminator) compete to generate data.",
    "category": "Generative Model"
  },
  {
    "code": "Autoencoder",
    "slug": "autoencoder",
    "name": "Autoencoder",
    "description": "An unsupervised learning model that compresses and reconstructs input data.",
    "category": "Representation Learning"
  }
]