Deep Learning and Neural Networks

  1. Generative Models
    1. Generative vs. Discriminative Models
      1. Definitions and Differences
        1. Probability Modeling Approaches
          1. Use Cases and Applications
          2. Autoencoder Foundations
            1. Encoder-Decoder Architecture
              1. Dimensionality Reduction
                1. Reconstruction Loss
                  1. Latent Space Representation
                  2. Variational Autoencoders (VAEs)
                    1. Probabilistic Approach to Autoencoders
                      1. The Encoder Network
                        1. Variational Inference
                          1. Mean and Variance Prediction
                          2. The Decoder Network
                            1. Generative Process
                              1. Reconstruction Probability
                              2. The Latent Space
                                1. Prior Distribution
                                  1. Posterior Approximation
                                    1. Sampling and Representation
                                    2. The Reparameterization Trick
                                      1. Enabling Backpropagation
                                        1. Gradient Flow Through Stochastic Nodes
                                        2. VAE Loss Function
                                          1. Reconstruction Loss
                                            1. KL Divergence Regularization
                                              1. Evidence Lower Bound (ELBO)
                                              2. VAE Variants
                                                1. Beta-VAE
                                                  1. Conditional VAE
                                                2. Generative Adversarial Networks (GANs)
                                                  1. Adversarial Training Concept
                                                    1. The Generator Network
                                                      1. Architecture and Objective
                                                        1. Noise-to-Data Mapping
                                                        2. The Discriminator Network
                                                          1. Architecture and Objective
                                                            1. Real vs. Fake Classification
                                                            2. The Adversarial Training Process
                                                              1. Minimax Game Formulation
                                                                1. Nash Equilibrium
                                                                  1. Training Dynamics
                                                                  2. GAN Training Challenges
                                                                    1. Mode Collapse
                                                                      1. Training Instability
                                                                        1. Vanishing Gradients
                                                                        2. GAN Variants
                                                                          1. Conditional GANs (cGANs)
                                                                            1. Deep Convolutional GANs (DCGANs)
                                                                              1. Wasserstein GANs (WGANs)
                                                                                1. CycleGANs
                                                                                  1. StyleGAN
                                                                                  2. GAN Evaluation Metrics
                                                                                    1. Inception Score
                                                                                      1. Frechet Inception Distance
                                                                                    2. Autoregressive Models
                                                                                      1. Sequential Generation
                                                                                        1. PixelRNN and PixelCNN
                                                                                          1. Autoregressive Language Models
                                                                                          2. Flow-Based Models
                                                                                            1. Normalizing Flows
                                                                                              1. Invertible Transformations
                                                                                                1. Exact Likelihood Computation
                                                                                                2. Diffusion Models
                                                                                                  1. Forward and Reverse Processes
                                                                                                    1. Denoising Diffusion Probabilistic Models
                                                                                                      1. Score-Based Generative Models
                                                                                                      2. Applications of Generative Models
                                                                                                        1. Image Synthesis
                                                                                                          1. Style Transfer
                                                                                                            1. Data Augmentation
                                                                                                              1. Anomaly Detection
                                                                                                                1. Creative Applications
                                                                                                                  1. Drug Discovery

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                                                                                                                7. The Transformer Architecture

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                                                                                                                9. Deep Reinforcement Learning