readme files

Author: Ubuntu

Diff for: README.ipynb

@@ -11,20 +11,19 @@
     "3. AWS 클라우드에서 Deep Learning 환경 만들기\n",
     "4. Deep Learning 프레임워크 101\n",
     " - Apache MXNet\n",
-    "   - [MXNet NDArray](./labs/MXNet-NDArray.ipynb)\n",
-    "   - [MXNet Symbol](./labs/MXNet-Symbol.ipynb)\n",
-    "   - [MXNet Module](./labs/MXNet-Module.ipynb)\n",
-    " - TensorFlow\n",
-    "5. Deep Learning Part 1 – Linear Regression 및 Classification\n",
+    "    - [MXNet NDArray](./labs/MXNet-NDArray.ipynb)\n",
+    "    - [MXNet Symbol](./labs/MXNet-Symbol.ipynb)\n",
+    "    - [MXNet Module](./labs/MXNet-Module.ipynb)\n",
+    "5. Deep Learning Part 1 - Linear Regression 및 Classification\n",
     "   - [MXNet Linear regression](./labs/MXNet-Linear_Regression.ipynb)\n",
     "   - [TensorFlow Linear regression](./labs/TensorFlow-Linear_regression.ipynb)\n",
     "   - [MXNet Multiclass regression](./labs/MXNet-Multi_Class_regression.ipynb)\n",
-    "6. Deep Learning Part 2 – Convolutional Neural Network (CNN)\n",
-    " - [MXNet을 이용한 MNIST 분류하는 CNN](./labs/CNN-mnist.ipynb)\n",
-    " - [MXNet의 Gluon을 이용한 CNN](./labs/CNN-mnist-gluon.ipynb)\n",
-    "7. Deep Learning Part 3 – Recurrent Neural Network (RNN)\n",
-    " - [TensorFlow를 이용한 SIN 함수 예측](./labs/RNN_LSTM_sin_prediction.ipynb)\n",
-    " - [TensorFlow를 이용한 주가 예측](./labs/RNN_LSTM_StockPricePredcition.ipynb)\n"
+    "6. Deep Learning Part 2 - Convolutional Neural Network (CNN)\n",
+    " - [MXNet을 이용한 MNIST 분류하는 CNN](./labs/CNN-mnist.ipynb)\n",
+    " - [MXNet의 Gluon을 이용한 CNN](./labs/CNN-mnist-gluon.ipynb)\n",
+    "7. Deep Learning Part 3 - Recurrent Neural Network (RNN)\n",
+    " - [TensorFlow를 이용한 SIN 함수 예측](./labs/RNN_LSTM_sin_prediction.ipynb)\n",
+    " - [LSTM을 이용한 주가 예측](./labs/RNN_LSTM_StockPricePredcition.ipynb)\n"
    ]
   },
   {

Diff for: README.md

@@ -5,17 +5,17 @@
 3. AWS 클라우드에서 Deep Learning 환경 만들기
 4. Deep Learning 프레임워크 101
  - Apache MXNet
-   - [MXNet NDArray](./labs/MXNet-NDArray.ipynb)
-   - [MXNet Symbol](./labs/MXNet-Symbol.ipynb)
-   - [MXNet Module](./labs/MXNet-Module.ipynb)
- - TensorFlow
-5. Deep Learning Part 1 – Linear Regression 및 Classification
+    - [MXNet NDArray](./labs/MXNet-NDArray.ipynb)
+    - [MXNet Symbol](./labs/MXNet-Symbol.ipynb)
+    - [MXNet Module](./labs/MXNet-Module.ipynb)
+5. Deep Learning Part 1 - Linear Regression 및 Classification
    - [MXNet Linear regression](./labs/MXNet-Linear_Regression.ipynb)
    - [TensorFlow Linear regression](./labs/TensorFlow-Linear_regression.ipynb)
    - [MXNet Multiclass regression](./labs/MXNet-Multi_Class_regression.ipynb)
-6. Deep Learning Part 2 – Convolutional Neural Network (CNN)
+6. Deep Learning Part 2 - Convolutional Neural Network (CNN)
  - [MXNet을 이용한 MNIST 분류하는 CNN](./labs/CNN-mnist.ipynb)
  - [MXNet의 Gluon을 이용한 CNN](./labs/CNN-mnist-gluon.ipynb)
-7. Deep Learning Part 3 – Recurrent Neural Network (RNN)
+7. Deep Learning Part 3 - Recurrent Neural Network (RNN)
  - [TensorFlow를 이용한 SIN 함수 예측](./labs/RNN_LSTM_sin_prediction.ipynb)
- - [TensorFlow를 이용한 주가 예측](./labs/RNN_LSTM_StockPricePredcition.ipynb)
+ - [LSTM을 이용한 주가 예측](./labs/RNN_LSTM_StockPricePredcition.ipynb)
+