Skip to content

LANGUAGE MODEL - 5

Timeline: 8th - 9th February, 2025

Introduction

Welcome to my documentation for Makemore Part 5 from Andrej Karpathy's Neural Networks: Zero to Hero series. In this section, we evolve our model from a simple 2-layer MLP into a deeper, tree-like convolutional architecture inspired by WaveNet (2016) from DeepMind. This part not only demonstrates how to scale up the network but also provides insights into the inner workings of torch.nn and the iterative deep learning development process.

Overview of Makemore Part 5

In this installment, the following key topics are explored:

Transitioning from MLP to a Deep Convolutional Network:
We start by transforming the basic 2-layer MLP into a more complex, hierarchical architecture. This transformation is achieved by adopting a tree-like structure that significantly expands the model’s receptive field and learning capacity.

Key Concepts Covered:

  • Deepening the Architecture:
    The video illustrates how to make a neural network deeper by introducing additional layers and branching structures. This deeper model is more adept at capturing complex patterns in the data.

  • Tree-Like Convolutional Structure:
    The approach mimics the WaveNet architecture by using a hierarchical layout. Although WaveNet employs causal dilated convolutions for efficient modeling (which are not yet covered in this series), the current implementation lays the groundwork for such advanced techniques.

  • From Fully Connected Layers to Convolutional Layers:
    By converting parts of the MLP into convolutional layers, the model is better equipped to handle sequential and spatial data. This step highlights the versatility of convolutional networks in various deep learning tasks.

  • Understanding torch.nn Internals:
    A detailed look is provided into how torch.nn modules operate behind the scenes. This deep dive is essential for debugging, optimizing, and extending neural network architectures in PyTorch.

  • Iterative Deep Learning Development Process:
    The video emphasizes the iterative nature of designing and refining deep neural networks. From experimenting with network depth to monitoring performance metrics, the development process is showcased as both systematic and exploratory.

Key Resources

Video Lecture

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 5

Structure of Contents

  • The lecture documentation has a single file notes and notebook respectively where everything is covered.
  • Notes have been marked with timestamps to the video.

 

Have fun, Happy Learning!