- Easy for me hard for you
- A simple prediction machine
- Classifying is not different from Predicting
- Training a Simple Classifier
Easy for me hard for you
Computers are fast calculators. They are good at doing arithmetic. On the flip side they are bad at recognizing images or doing other human like tasks.
A simple prediction machine
Assuming we want to build a prediction machine, a computer would take an input and process it to produce an output.
input => process… => output
Assuming we want to build machine which is capable of figuring out how to convert from kilometers to miles when we feed it some data.
| kilometer | miles |
|---|---|
| 0 | 0 |
| 100 | 62 |
Thus we come up with a guess to multiply the kilometers by:
\[100\ km = km \times 0.5 = 50\ miles \\\\ error = truth - calculated\ result\]Turns out we are wrong by 12 miles which is our error. We can use the error to figure out a new guess.
\[100\ km = km \times 0.6 = 60\ miles \\\\ error = 2\ miles\]The output is still too small thus we have to adjust again:
\[100\ km = km \times 0.7 = 70\ miles \\\\ error = 8\ miles \\\\ 100\ km = km \times 0.61 = 61\ miles \\\\ error = 1\ mile\]As our guess approaches the correct answer (small error rate), our steps to ajust should decrease. Thus, our steps are a fraction of the error.
Classifying is not different from Predicting
Given data, we want to classify wether an insect is a caterpillar or a ladybugs. We can see that based on length and width. A caterpillar is longer than wide and the opposite is true for ladybugs.
source: (book) make your own neural networks
The line added seperates both insects based on its characteristics. If we were to encounter an unknown insect we can use this classifier to figure out what it is.
we use training data to teach our classifier the truth
Training a Simple Classifier
$$