Jianhao Ma's IMA Blog

 Reading Response and Markov Chain

 A6: Quick Draw! About Quick Draw Data Set Quick Draw is a representative machine learning & data collection project, it shows an ideal design model for public machine learning and data set projects.  It's simply designed to be engaging and accessible. And such accessibility profligates the data collection progress. It uses the classification model trained with collected data as a feedback to reward participants.  Although there are several potential problems with the dataset such as latent bias, I still found it very helpful for beginners. Both the doodle and the label are small objects (compared to other visual machine learning projects), therefore are easy and fast to process. It makes ambitious attempts possible for machine learning learners.  DoodleNet p5 Sketch https://editor.p5js.org/roger1mjh/sketches/kPVq2Prxl For this week's homework, I worked on the "two canvases" template. My idea is to visually represent and store the "labels", "confide...

Exploring Datasets and Practice on 311 World Happiness Report I've seen other datasets from Gallup World Poll before and was interested in how it quantifies happiness and its parameters. Here's the details of this dataset:     Collector/contributor:   United Nations     Purpose : Happiness Indicators to inform government's decision makings     Collection Method:  Data are collected from Gallup World Poll     Dimensions:  156 lines(countries), 12 columns The dataset quantifies happiness of citizens from different countries from a macro perspectives. It combines individual polls and objective statistics. It's convenient to use machine learning and do a regression to estimate the correlation between Happiness and each parameters(GDP, life expectancy, etc..) 311 p5 sketch exercise https://editor.p5js.org/roger1mjh/sketches/ZG2BkJS07 Originally I want to make some edits on the existed code to conduct a different use of the simplifi...

 Image Transfer on P5 Response to Dr. Fiebrink's Speech: There's one part in this speech that intrigues me the most, which is using Machine Learning in creative works does not need to be smart and delicate. It is OK to be dumb and funny, because that's what an unmatured technology genuinely looks like, and it can still be creative. The Edges to Cat example is so fun to play with that I'm kind of addict to it.  Attempt to do Image Transfer on P5 with Pose Regression I watched the videos posted and was most interested in the pose regression example. But the RGB value shift looks not "machine learningly" enough, because it's possible to make some similar project without neural network. Therefore I tried to make one with image transfer. Firstly, I need to load image in data format in P5. I decided to try pixels first, because they retrieve and store image data directly. Then I console logged the "pixels" object in draw, to test out how much the data ...