| 4과목 1장 |
코드 |
147-148 |
6-1) Hypothesis using matrix |
[4과목1장] 6-1. Hypothesis using matrix.ipynb
|
-
|
| 4과목 1장 |
코드 |
148-149 |
6-2) matrix |
[4과목1장] 6-2. matrix.ipynb
|
https://github.com/hunkim/DeepLearningZeroToAll/blob/master/lab-04-2-multi_variable_matmul_linear_regression.py
|
| 4과목 1장 |
데이터 |
149-151 |
6-3) Loading data from file |
data-01-test-score.csv
|
https://github.com/hunkim/DeepLearningZeroToAll
|
| 4과목 1장 |
코드 |
149-151 |
6-3) Loading data from file |
[4과목1장] 6-3. Loading data from file.ipynb
|
-
|
| 4과목 1장 |
코드 |
161-162 |
9-1) Logistic (regression) classifier - 실습 |
[4과목1장] 9-1. Logistic (regression) classifier - 실습.ipynb
|
-
|
| 4과목 1장 |
코드 |
170-172 |
12) Softmax classifier |
[4과목1장] 12. Softmax Classifier - 실습.ipynb
|
-
|
| 4과목 1장 |
데이터 |
173 |
13-1) softmax_cross_entropy_with_logits |
data-04-zoo.csv
|
https://github.com/hunkim/DeepLearningZeroToAll
|
| 4과목 1장 |
내용출처 |
173 |
13-2) tf.one_hot and reshape |
-
|
https://www.tensorflow.org/api_docs/python/tf/one_hot
|
| 4과목 1장 |
데이터 |
174 |
13-2) tf.one_hot and reshape |
zoo1.csv
|
https://github.com/droglenc/NCData
|
| 4과목 1장 |
코드 |
172-175 |
13) Softmax Cross entropy with logits - 실습 |
[4과목1장] 13. Softmax Cross entropy with logits - 실습.ipynb
|
-
|
| 4과목 1장 |
코드 |
180-182 |
16) Learning rate, Evaluation - 실습 |
[4과목1장] 16. Learning rate Evaluation - 실습.ipynb
|
https://github.com/hunkim/DeepLearningZeroToAll/blob/master/lab-07-1-learning_rate_and_evaluation.py
|