🤓 Yashwanth's Notes

        • 1. Understanding Large Language Models
        • 2. Working with Text Data
        • 3. Coding Attention Mechanisms
        • 4. Implementing a GPT Model From Scratch to Generate Text
        • 5. Pretraining on Unlabeled Data
      • DDPM from Scratch
      • SD from Scratch
        • Inner Products
        • Lengths and Angles of Vectors
        • Matrix Representations of inner products
        • Norms
      • Autocorrelation
      • Hessian Matrix
      • Quasi-Newton Methods
      • Radial Basis Functions (RBFs)
      • Structural risk minimization
      • Symmetric Positive Definite Matrices (SPD Matrices)
      • The Conjugate Gradient Method
      • The Polar Decomposition
      • AdaMuon - Adaptive Muon Optimizer
      • AlexNet - ImageNet Classification with Deep Convolutional Neural Networks
      • Hands-on Bayesian Neural Networks – A Tutorial for Deep Learning Users
      • High-Resolution Image Synthesis with Latent Diffusion Models
      • Identity Mappings in Deep Residual Networks
      • Keeping Neural Networks Simple by Minimizing the Description Length of the Weights
      • LeNet - Gradient-Based Learning Applied to Document Recognition
      • ResNet - Deep Residual Learning for Image Recognition
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    From Scratch

    Folder: From-Scratch

    3 items under this folder.

    • Aug 24, 2025

      SD from Scratch

      • Feb 03, 2025

        DDPM from Scratch

        • Dec 16, 2024

          LLM-from-Scratch

          • folder

              • 1. Understanding Large Language Models
              • 2. Working with Text Data
              • 3. Coding Attention Mechanisms
              • 4. Implementing a GPT Model From Scratch to Generate Text
              • 5. Pretraining on Unlabeled Data
            • DDPM from Scratch
            • SD from Scratch
              • Inner Products
              • Lengths and Angles of Vectors
              • Matrix Representations of inner products
              • Norms
            • Autocorrelation
            • Hessian Matrix
            • Quasi-Newton Methods
            • Radial Basis Functions (RBFs)
            • Structural risk minimization
            • Symmetric Positive Definite Matrices (SPD Matrices)
            • The Conjugate Gradient Method
            • The Polar Decomposition
            • AdaMuon - Adaptive Muon Optimizer
            • AlexNet - ImageNet Classification with Deep Convolutional Neural Networks
            • Hands-on Bayesian Neural Networks – A Tutorial for Deep Learning Users
            • High-Resolution Image Synthesis with Latent Diffusion Models
            • Identity Mappings in Deep Residual Networks
            • Keeping Neural Networks Simple by Minimizing the Description Length of the Weights
            • LeNet - Gradient-Based Learning Applied to Document Recognition
            • ResNet - Deep Residual Learning for Image Recognition

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