🤓 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
        • 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
      • AlexNet - ImageNet Classification with Deep Convolutional Neural Networks
      • 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

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    LLM from Scratch

    Folder: From-Scratch/LLM-from-Scratch

    5 items under this folder.

    • Dec 16, 2024

      5. Pretraining on Unlabeled Data

      • Dec 13, 2024

        3. Coding Attention Mechanisms

        • Dec 13, 2024

          4. Implementing a GPT Model From Scratch to Generate Text

          • Dec 09, 2024

            2. Working with Text Data

            • Dec 06, 2024

              1. Understanding Large Language Models


                    • 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
                    • 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
                  • AlexNet - ImageNet Classification with Deep Convolutional Neural Networks
                  • 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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