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Deep Learning

Machine learning with many-layered neural networks that learn representations directly from raw data.

Deep Learning is one of the core areas in the AI University map of AI. Explore the diagram, then dive into each topic — every subtopic grows into its own deep-dive over time.

flowchart TB
  NN([Neural Networks]) --> CNN[CNNs<br/>vision]
  NN --> RNN[RNNs / LSTMs<br/>sequences]
  NN --> TF[Transformers<br/>attention]
  NN --> GEN[Diffusion & GANs<br/>generation]
  NN --> GNN[Graph NNs<br/>graphs]

Key topics

  • Neural networks

    Layers of weighted connections and non-linear activations, trained by backpropagation.

  • CNNs

    Convolutional networks exploit spatial structure — the workhorse of classic computer vision.

  • RNNs & LSTMs

    Recurrent networks for sequences; LSTMs/GRUs address long-range memory (largely superseded by transformers).

  • Transformers & attention

    Self-attention lets every token attend to every other; the architecture behind modern LLMs and much of vision.

  • Diffusion models & GANs

    Two families of generative models powering image, audio, and video synthesis.

  • Graph neural networks

    Networks that operate on graph-structured data — molecules, social networks, knowledge graphs.

  • Training at scale

    GPUs/TPUs, mixed precision, distributed and parallel training, and the scaling laws that drive frontier models.

Machine Learning · NLP & Large Language Models · Generative AI · Computer Vision

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