March 2020
Beginner to intermediate
342 pages
8h 38m
English
If we hope to understand classification intuitively, then we need a dataset that we can visualize easily. MNIST, with its mind-boggling hundreds of dimensions, is way too complex for that. Instead, we’ll use a simpler, brain-friendly dataset:
| | Input_A Input_B Label |
| | -0.470680718301 -1.905835436960 1 |
| | 0.9952553595720 1.4019246363100 0 |
| | -0.903484238413 -1.233058043620 1 |
| | -1.775876322450 -0.436802254656 1 |
Those are just the first few lines. The file contains 300 examples in total, each with two input variables and a binary label. I wrote a program to plot these data, that you can find in the book’s source code as usual. (It’s called plot_data.py.) It uses the two input variables as coordinates, ...