deep-learning-with-keras-notebooks

這個github的repository主要是個人在學習Keras的一些記錄及練習。希望在學習過程中發現到一些好的資訊與範例也可以對想要學習使用 Keras來解決問題的同好，或是對深度學習有興趣的在學學生可以有一些方便理解與上手範例來練練手。如果你/妳也有相關的範例想要一同分享給更多的人, 也歡迎issue PR來給我。

這些notebooks主要是使用Python 3.6與Keras 2.1.1版本跑在一台配置Nivida 1080Ti的Windows 10的機台所產生的結果, 但有些部份會參雜一些Tensorflow與其它的函式庫的介紹。對於想要進行Deeplearning的朋友們, 真心建議要有GPU啊~!

如果你/妳覺得這個repo對學習deep-learning有幫助, 除了給它一個star以外也請大家不吝嗇去推廣給更多的人。

內容

0.圖像資料集/工具介詔

1.Keras API範例

2.圖像辨識 (Image Classification)

3.物體偵測 (Object Recognition)

4.物體分割 (Object Segmentation)

5.關鍵點偵測 (Keypoint Detection)

6.圖像標題 (Image Caption)

7.人臉偵測辨識 (Face Detection/Recognition)

8.自然語言處理 (Natural Language Processing)

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deep-learning-with-keras-notebooks

內容

0.圖像資料集/工具介詔

1.Keras API範例

2.圖像辨識 (Image Classification)

3.物體偵測 (Object Recognition)

4.物體分割 (Object Segmentation)

5.關鍵點偵測 (Keypoint Detection)

6.圖像標題 (Image Caption)

7.人臉偵測辨識 (Face Detection/Recognition)

8.自然語言處理 (Natural Language Processing)

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Name		Name	Last commit message	Last commit date
Latest commit History 55 Commits
assets/data		assets/data
0.0-coco-dataset-api.ipynb		0.0-coco-dataset-api.ipynb
0.1-poker-cards-dataset.ipynb		0.1-poker-cards-dataset.ipynb
0.2-image-processing-pillow.ipynb		0.2-image-processing-pillow.ipynb
1.0-image-augmentation.ipynb		1.0-image-augmentation.ipynb
1.1-keras-functional-api.ipynb		1.1-keras-functional-api.ipynb
1.2-vgg16-from-scratch.ipynb		1.2-vgg16-from-scratch.ipynb
1.3-use-pretrained-model.ipynb		1.3-use-pretrained-model.ipynb
1.4-small-datasets-image-augmentation.ipynb		1.4-small-datasets-image-augmentation.ipynb
1.5-use-pretrained-model-2.ipynb		1.5-use-pretrained-model-2.ipynb
1.6-visualizing-what-convnets-learn.ipynb		1.6-visualizing-what-convnets-learn.ipynb
1.7-autoencoder.ipynb		1.7-autoencoder.ipynb
1.8-seq2seq-introduction.ipynb		1.8-seq2seq-introduction.ipynb
1.9-onehot-encoding-introduction.ipynb		1.9-onehot-encoding-introduction.ipynb
1.a-rnn-introduction.ipynb		1.a-rnn-introduction.ipynb
1.b-lstm-return-sequences-states.ipynb		1.b-lstm-return-sequences-states.ipynb
1.c-lstm-learn-alphabetic-seq.ipynb		1.c-lstm-learn-alphabetic-seq.ipynb
2.0-first-steps-with-julia.ipynb		2.0-first-steps-with-julia.ipynb
2.1-traffic-signs-recognition.ipynb		2.1-traffic-signs-recognition.ipynb
2.2-simpson-characters-recognition.ipynb		2.2-simpson-characters-recognition.ipynb
2.3-fashion-mnist-recognition.ipynb		2.3-fashion-mnist-recognition.ipynb
2.4-facial-keypoints-recognition.ipynb		2.4-facial-keypoints-recognition.ipynb
2.5-use-keras-break-captcha.ipynb		2.5-use-keras-break-captcha.ipynb
2.6-mnist-recognition-mlp.ipynb		2.6-mnist-recognition-mlp.ipynb
2.7-mnist-recognition-cnn.ipynb		2.7-mnist-recognition-cnn.ipynb
3.0-yolo-algorithm-introduction.ipynb		3.0-yolo-algorithm-introduction.ipynb
3.1-yolov2-object-detection.ipynb		3.1-yolov2-object-detection.ipynb
3.2-yolov2-train_racoon_dataset.ipynb		3.2-yolov2-train_racoon_dataset.ipynb
3.3-yolov2-racoon_detection_inaction.ipynb		3.3-yolov2-racoon_detection_inaction.ipynb
3.4-yolov2-train-kangaroo-dataset.ipynb		3.4-yolov2-train-kangaroo-dataset.ipynb
3.5-yolov2-train-hands-dataset.ipynb		3.5-yolov2-train-hands-dataset.ipynb
3.6-yolov2-train-simpson-dataset.ipynb		3.6-yolov2-train-simpson-dataset.ipynb
3.7-yolov2-train-coco-dataset.ipynb		3.7-yolov2-train-coco-dataset.ipynb
7.0-opencv-face-detection.ipynb		7.0-opencv-face-detection.ipynb
7.1-mtcnn-face-detection.ipynb		7.1-mtcnn-face-detection.ipynb
7.2-face-detect-align-and-crop.ipynb		7.2-face-detect-align-and-crop.ipynb
7.3-face-embedding-and-classifier.ipynb		7.3-face-embedding-and-classifier.ipynb
7.4-face-recognition.ipynb		7.4-face-recognition.ipynb
7.5-face-landmarks-detection.ipynb		7.5-face-landmarks-detection.ipynb
7.6-head-pose-estimation.ipynb		7.6-head-pose-estimation.ipynb
8.0-using-word-embeddings.ipynb		8.0-using-word-embeddings.ipynb
8.1-jieba-word-tokenizer.ipynb		8.1-jieba-word-tokenizer.ipynb
8.2-word2vec-concept-introduction.ipynb		8.2-word2vec-concept-introduction.ipynb
8.3-jieba-lyrics-analysis.ipynb		8.3-jieba-lyrics-analysis.ipynb
8.4-word2vec-with-gensim.ipynb		8.4-word2vec-with-gensim.ipynb
README.md		README.md

digital-life-lab/deep-learning-with-keras-notebooks

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deep-learning-with-keras-notebooks

內容

0.圖像資料集/工具介詔

1.Keras API範例

2.圖像辨識 (Image Classification)

3.物體偵測 (Object Recognition)

4.物體分割 (Object Segmentation)

5.關鍵點偵測 (Keypoint Detection)

6.圖像標題 (Image Caption)

7.人臉偵測辨識 (Face Detection/Recognition)

8.自然語言處理 (Natural Language Processing)

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