LANGUAGE MODEL - 2

Introduction

Welcome to my documentation for Makemore Part 2 from Andrej Karpathy's Neural Networks: Zero to Hero series. This section focuses on implementing a Multilayer Perceptron (MLP) as a character-level language model. Here, I’ve compiled my notes and insights from the lecture to serve as a reference for understanding the key concepts and practical implementations discussed.

Overview of Makemore Part 2

In this part of the series, I explored the following topics:

Implementing a Multilayer Perceptron (MLP): The MLP architecture is fundamental in neural networks, and this lecture provided a hands-on approach to understanding how multiple layers can learn complex data representations.

Key Concepts Covered:

• Model training techniques
• Learning rate tuning strategies
• Hyperparameter adjustments
• Evaluation metrics including loss functions and accuracy
• Insights into overfitting and underfitting

Key Resources

Video Lecture

• I watched the lecture on YouTube: Building Makemore Part 2

Codes:

• The Jupyter notebooks and code implementations are available within this documentation itself.
• If you wish to view the repository where I originally worked on, you can view it here: Neural Networks - Language Model 2

Structure of Contents

• The lecture documentation has been divided into 3 sets: Set A, Set B, and Set C.
• Each set has its own notes and notebook.
• Notes have been marked with timestamps to the video.
• This allows for simplicity and better understanding, as the lecture is long.

 

Have fun, Happy Learning!