Introduction to Neural Networks
ai
hpc
A beginner’s guide to understanding neural networks, their architecture, and how they function.
This tutorial covers the basics of neural networks (aka “deep learning”), which is a technique within machine learning that tends to outperform other techniques when dealing with a large amount of data.
- 🎯 Goals:
- Introduce deep learning fundamentals through hands-on activities
- Provide the necessary background for the rest of the project
- 📖 Definitions:
- Artificial intelligence (AI) is a set of approaches to solving complex problems by imitating the brain’s ability to learn.
- Machine learning (ML) is the field of study that gives computers the ability to learn without being explicitly programmed (i.e. learning patterns instead of writing down rules.) Arguably, machine learning is now a subfield of AI.
- 🤔 Recap: Last week, we learned about using linear regression to predict the sale price of a house. We fit a function to the dataset:
- Input: above ground square feet
- Output: sale price
- Function type: linear
- Loss function: mean squared error
- Optimization algorithm: stochastic gradient descent
The MNIST dataset contains thousands of examples of handwritten numbers, with each digit labeled 0-9.
We’ll start with the MNIST problem in this notebook:
📓 Fitting MNIST with a multi-layer perceptron (MLP)
Next week, we’ll learn about other types of neural networks.
References:
- Here are some recommendations for further reading:
Citation
BibTeX citation:
@online{foreman2025,
author = {Foreman, Sam},
title = {Introduction to {Neural} {Networks}},
date = {2025-07-15},
url = {https://saforem2.github.io/hpc-bootcamp-2025/01-neural-networks/0-intro/},
langid = {en}
}
For attribution, please cite this work as:
Foreman, Sam. 2025. “Introduction to Neural Networks.” July
15, 2025. https://saforem2.github.io/hpc-bootcamp-2025/01-neural-networks/0-intro/.