Joel Grus - Livecoding Madness - Let's Build a Deep Learning Library
3 min read
1 month ago
Published on Jul 29, 2024
This response is partially generated with the help of AI. It may contain inaccuracies.
Table of Contents
Title: Building a Deep Learning Library - Step-by-Step Tutorial
Step 1: Introduction
- Who is Joel Grus?
- Joel Grus is a research engineer at the Allen Institute for Artificial Intelligence in Seattle.
- He is the author of the book "Data Science from Scratch" and co-host of the podcast "Adversarial Learning."
Step 2: Setting up the Environment
- Using Python 3.6
- Make sure you have Python 3.6 installed on your system.
- Joel uses type hints extensively in his code.
Step 3: Creating the Library Structure
- Create a Directory
- Create a directory named "gillnet" to house the deep learning library.
- Add an
__init__.py
file to the folder.
Step 4: Implementing Tensors
- Define the Tensor Class
- Implement a tensor class with methods for loss calculation and gradients.
- Use NumPy for numerical computations.
Step 5: Implementing Layers
- Create Layer Classes
- Implement a base layer class with forward and backward propagation methods.
- Create specific layers like linear layers and activation layers (e.g., tanh).
Step 6: Defining Neural Networks
- Build Neural Networks
- Define a neural network class that consists of multiple layers.
- Implement methods for forward and backward propagation through the network.
Step 7: Implementing Optimizers
- Optimizing Parameters
- Create an optimizer class (e.g., Stochastic Gradient Descent) to adjust network parameters based on gradients.
Step 8: Data Handling
- Data Iteration
- Implement a data iterator to handle input-output data in batches.
- Use binary encoding for data representation.
Step 9: Training the Neural Network
- Training Process
- Define a training function that iterates over epochs and batches to train the neural network.
- Use a loss function (e.g., mean squared error) to compute errors and gradients.
Step 10: Testing with FizzBuzz Problem
- Solving FizzBuzz with Neural Network
- Apply the trained neural network to solve the FizzBuzz problem.
- Encode input numbers and predict corresponding FizzBuzz outputs.
Step 11: Conclusion and Resources
- Wrap-Up and Resources
- Review the steps taken to build the deep learning library.
- Check out Joel Grus' GitHub repository for the code.
- Explore more about artificial intelligence at Allen Institute for AI.
By following these steps, you can create your own deep learning library inspired by Joel Grus' live coding session.