Author: Marieme Ngom ([email protected]), combining and adapting materials evolved over time by Bethany Lusch, Asad Khan, Prasanna Balaprakash, Taylor Childers, Corey Adams, Kyle Felker, and Tanwi Mallick
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.
This is a quick overview, but the goals are:
- to introduce the fundamental concepts of deep learning through hands-on activities
- to give you the necessary background for the more advanced topics in the coming weeks.
Some rough 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.
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
This week, we'll work on a "classification" problem, which means that we have a category label for each data point, and we fit a function that can categorize inputs.
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.
We will also be working on Google Collab this week.
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Sign in at https://colab.research.google.com/
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Go to
File -> Githubthen searchargonne-lcfthenargonne-lcf/ai-science-training-seriesand pick01_introduction_mnist.ipynb.
Here are some recommendations for further reading: