intuitiveai

Deep Learning and Neural Architectures

Study neural networks, backpropagation, convolution, recurrence, graph learning, attention, and efficient sequence models with mathematical prerequisites.

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Prerequisites

  1. 01
    Neural Networks

    The building blocks of deep learning: Perceptrons and MLPs.

  2. 02
    Backpropagation

    How neural networks learn using the chain rule.

  3. 03
    CNNs

    Convolutional Neural Networks for image recognition.

  4. 04
    RNNs & LSTMs

    Backpropagation Through Time (BPTT), vanishing gradients, forget gates, and long-range sequence memory.

  5. 05
    Graph Neural Networks (GNNs)

    Message passing paradigms, Graph Convolutional Networks (GCN), GraphSAGE, and non-Euclidean graph representations.

  6. 06
    Transformers

    The attention mechanism that powers modern AI.

  7. 07
    Attention Variants & Norms

    Grouped-Query Attention (GQA), Multi-Query (MQA), RoPE embeddings, and RMSNorm topology.

  8. 08
    State Space Models & Mamba

    Continuous-time SSMs, selective scan parameters, discretization, and linear-time sequence modeling.