Image Classifier

There is no obvious way

Cannot hard code an algorithm

Attempts

Find edges and corners

Categorize edges and boundaries
Not scalable for multiple categories

Data-Driven Approach

Collect a dataset of images and labels
Use Machine Learning to train a classifier
Evaluate the classifier on new images

def train(images, labels):
	# Machine Learning!
	return model

def predict(model, test_images):
	# Use model to predict labels
	return test_labels

How do we compare images?

L1 Distance

L2 Distance

Hyperparameters

Different Classifiers

We want fast predictions

More practical when put in use

Want low latency

Slow training is fine

Can be done in the background

Nearest Neighbour

Linear Classification

Softmax Classifier

Use Loss Functions & Optimizations to determine how good the classifier is

Todo

Define a loss function that quantifies our unhappiness with the scores across the training data
Come up with a way of efficiently finding the parameters that minimize the loss function (optimization)