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Recurrent networks for sequences; LSTMs/GRUs address long-range memory (largely superseded by transformers).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

Why depth works: representation learning¶

A deep network is a stack of layers, each transforming its input into a slightly more useful representation. Early layers in a vision model learn edges; middle layers learn textures and parts; late layers learn whole objects. Nobody programs this hierarchy — it emerges from training. That is the core magic of deep learning: it learns its own features, removing the hand-crafted feature engineering that classical ML depended on.

The transformer, briefly¶

Since 2017, one architecture dominates: the transformer. Its key mechanism is self-attention — every element of a sequence can look at every other element and decide what is relevant.

flowchart LR
  IN[/Token sequence/] --> Q[Query]
  IN --> K[Key]
  IN --> V[Value]
  Q --> ATT{{Attention<br/>who attends to whom}}
  K --> ATT
  V --> ATT
  ATT --> OUT[/Context-aware representation/]

Attention replaced the step-by-step recurrence of older RNNs with something highly parallel, which is exactly what modern GPUs are good at. That parallelism — not just accuracy — is why transformers scaled to billions of parameters.

Scaling laws¶

One of the most consequential findings in modern AI: performance improves predictably as you increase three things together — model size, data, and compute. These "scaling laws" mean you can forecast how much better a bigger model will be before training it, which is why labs invest in ever-larger runs. Scaling is not infinite or free, but it explains why "just make it bigger" has worked so surprisingly well.

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

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