[TOC]
Coding for machine learning practice!
| index | name | finished |
|---|---|---|
| 1 | only 1 hidden layer works | √ |
| 2 | dynamic network structure |
| index | name | finished |
|---|---|---|
| 1 | optimize dataset interface | √ |
| 2 | use python package pandas |
| index | name | finished |
|---|---|---|
| 1 | Linear Regression | √ |
| 2 | Logistic Regression | √ |
| 3 | Linear Discriminant Analysis | √ |
| 4 | Kernel LDA |
| index | name | finished |
|---|---|---|
| 1 | process sequential attribution | √ |
| 2 | pre-pruning, post-pruning | |
| 3 | process blank / missing value | |
| 4 | visualize decision tree |
| index | name | finished |
|---|---|---|
| 1 | improve classification performance | √ |
| 2 | implement multi kernel methods | √ |
| 3 | visualize data | √ |
| 4 | case of eta >= 0 |
| index | name | finished |
|---|---|---|
| 1 | perceptron | √ |
| 2 | visualize with turtle | √ |
| 3 | fully connected nn | √ |
| 4 | improve classification performance | √ |
| 5 | visualize classification result of fcnn | √ |
| 6 | visualize training loss | √ |
| 7 | visualize network architecture with turtle | √ |
| 8 | save and load parameter interface | √ |
| 9 | implement NN with OO style | √ |
| 10 | visualization with Qt | √ |
| 11 | dynamic node management (correctness) | |
| 12 | ui with pytorch |