- Review the full story of building a ML model for classification or regression.
- Understand how data is formatted and downloaded including CSV and JSON.
- Review the terminology of the training process
- Training
- Learning Rate
- Epochs
- Batch size
- Loss
- Consider how to frame the problem and collect data.
- Understand critical questions to ask (e.g. Who is this for? What’s the context?)
- Understand the questions to ask about sourcing and collecting data.
- Learn how to prepare a data set, including how to normalize and properly format it.
- Tabular Data (CSV)
- Tabular Data from Coding Train "Data + APIs" tutorial (lots of extra stuff here the first few minutes is probably most relevant?)
- Tabular Data Coding Train Processing tutorial (code is not JS!)
- https://www.kaggle.com/datasets/brendan45774/test-file, https://github.com/ml5js/ml5-library/blob/main/examples/p5js/NeuralNetwork/NeuralNetwork_titanic/data/titanic_cleaned.csv
- JSON Data
- What is JSON Part 1 - Coding Train p5.js tutorial
- What is JSON Part 2 - Coding Train p5.js tutorial
- JSON Data from Coding Train "Data + APIs" tutorial (same as above, lots of extra unrelated stuff here).
- Full Coding Train Video Series using TensorFlow.js, 7.1-7.5 cover how the data was collected and cleaned
- Crowdsourcing Colors website, Crowdsourcing Colors source code
- Data Wrangling slides
- Feminist Data Set by Caroline Sinders
- Gender Shades: How well do IBM, Microsoft, and Face++ AI services guess the gender of a face? by Joy Buolamwini and Timnit Gebru
- Data Research Questions
- Who collected and compiled it?
- Why was it collected?
- How was it collected?
- Describe the data: What are the dimensions? What are the variables and their data types?
- Is there missing, incorrect, or otherwise problematic data?
- For whom is this data accurate or useful? What is this data unrepresentative of? (Who is missing and left out of the data?)
- Knowing what you know now about machine learning, what will a model trained on this data help you do? Are there are alternative (non-machine learning) uses of this data?
- Color Classifer JSON Data
- Color Classifier CSV Data
- 311 Call data from Lydisa Jessup
- https://editor.p5js.org/yining/sketches/-i4GJnlgz
- color dataset: https://raw.githubusercontent.com/ml5js/ml5-library/main/examples/p5js/NeuralNetwork/NeuralNetwork_color_classifier/data/colorData_small.json
- https://editor.p5js.org/yining/sketches/zYXhVuyPw
- DoodleNet training template
- In class:
For this assignment it's up to you to select an exercise building off of the multiple data collection and model training scenarios we've examined in past a few Here are three scenarios to choose from to help you get started. (To be clear, just pick one or combine into one assignment, do not do all three!)
Continue working on your sketch from Assignment 8. What are your next steps? Maybe try breaking it into multiple sketches for saving / loading the trained model? Or try a regression model instead of classification?
Build off of one of the code examples (or invent your own) to develop you own creative use of Quick, Draw data.
- Classifying Drawings with ml5's DoodleNet: output in DOM element -- Can you make this one work with webcam input instead of canvas?
- Classifying Drawings with ml5's DoodleNet: output in separate canvas
- Can you try your own doodleNet with a fewer classes? demo, code, get doodleNet dataset, download .npy files.
Try loading JSON or tabular data into a p5.js sketch and train a model. Some suggestions:
- Augment Lydia Jessup's 311 Calls ml5.js example. You could add an additional input field, customize the interface, or change other parameters of
ml5.neuralNetwork(). - Train a machine learning model in ml5.js with the dataset you picked for part 1 of the assignment.
Complete a blog post with your dataset report and code exercise. Link from the homework wiki.