├── .gitignore ├── 01 標準的 Fully Connected (Dense) NN.ipynb ├── 01-1. 標準神經網路做手寫辨識（MNIST）.ipynb ├── 01-2 取回你訓練好的神經網路.ipynb ├── 01-3. 作業說明_改造你的神經網路.ipynb ├── 02-1. 用CNN圖形辨識（還是MNIST）.ipynb ├── 02-2. 作業說明_調校你的CNN.ipynb ├── 04-1. 用RNN做情意分析.ipynb ├── 05-1. 建立神經網路的其他方式並學習第一個轉移學習模型.ipynb ├── 05-2. 作業說明_轉移學習的練習 (以 CNN 手寫辨識為例).ipynb ├── 06-1. 建立神經網路的高級方式.ipynb ├── 07-1. 自編碼器 Autoencoder.ipynb ├── 07-2. 使用 CNN 建立 VAE.ipynb ├── README.md ├── Untitled.ipynb ├── VAE.ipynb ├── VAE_handwriting_model_weights.h5 ├── autoencoder.png ├── autoencoder_handwriting_model_weights.h5 ├── branch-and-merge.png ├── branch-and-merge_final.png ├── data_generator.ipynb ├── handwriting_model_architecture.json ├── handwriting_model_cnn.json ├── handwriting_model_weights.h5 ├── handwriting_weights_cnn.h5 ├── imdb_model_architecture.json ├── imdb_model_weights.h5 ├── imdb_rnn.h5 ├── model01.png ├── myrnn.h5 ├── plain_model.png ├── variational_autoencoder.png ├── 工研院2018 ├── CNN_fail.ipynb ├── NN_MNIST.ipynb ├── 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