7a05c5eea4011db1a375af54ee44ab57_file-786df78d78cf77834f4288451c926cb65dbf623e.ipynb

lab-image-as-data.ipynb

images-as-data

In this lab we'll look at images as data. In particular, we'll show how to use standard Python libraries to visualize and distort images, switch between matrix and vector representations, and run machine learning to perform a classification task. We'll be using the ever popular MNIST digit recognition data set. This will be a two part lab, comprised of the following steps:
Part1:

Generate random matrices and visualize as images
Data Prep and Exploration
-- Load MNIST data
-- Convert the vectors to a matrix and visualize
-- Look at pixel distributions
-- Heuristic feature reduction
-- Split data into train and validation

Part 2:
3. Classification
-- One vs. All using logisitic regression.
-- Random Forests on all
4. Error analysis
-- Visualize confusion matrix
-- Look at specific errors
5. Generate synthetic data and rerun RF

images-as-data

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