Generative AI

  1. Machine Learning Fundamentals
    1. Learning Paradigms
      1. Supervised Learning
        1. Classification Tasks
          1. Regression Tasks
            1. Evaluation Metrics
            2. Unsupervised Learning
              1. Clustering
                1. Dimensionality Reduction
                  1. Density Estimation
                  2. Semi-Supervised Learning
                    1. Label Propagation
                      1. Self-Training
                        1. Co-Training
                        2. Reinforcement Learning
                          1. Markov Decision Processes
                            1. Policy Learning
                              1. Value Functions
                            2. Neural Network Architectures
                              1. Feedforward Networks
                                1. Multilayer Perceptrons
                                  1. Universal Approximation Theorem
                                    1. Activation Functions
                                    2. Convolutional Neural Networks
                                      1. Convolution Operations
                                        1. Pooling Layers
                                          1. CNN Architectures
                                          2. Recurrent Neural Networks
                                            1. Vanilla RNNs
                                              1. LSTM Networks
                                                1. GRU Networks
                                                2. Attention Mechanisms
                                                  1. Self-Attention
                                                    1. Cross-Attention
                                                      1. Multi-Head Attention
                                                    2. Training and Optimization
                                                      1. Backpropagation Algorithm
                                                        1. Forward Pass
                                                          1. Backward Pass
                                                            1. Chain Rule Application
                                                            2. Optimization Algorithms
                                                              1. SGD and Variants
                                                                1. Adam Optimizer
                                                                  1. Learning Rate Scheduling
                                                                  2. Overfitting and Generalization
                                                                    1. Bias-Variance Tradeoff
                                                                      1. Cross-Validation
                                                                        1. Early Stopping

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                                                                    2. Mathematical and Technical Foundations

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                                                                    4. Core Generative Model Architectures