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1. Anomaly detection methods
2. How to clean Jupyter Notebook enviroment under JupyterHub
3. Configure JupyterHub for multiple users sharing one Ubuntu server
4. Model Performance Analysis
5. Customize your Python environment in Jupyterhub
6. What is numpy.newaxis and when to use it
7. Test Image Classifier and Show the Result
8. CNN Model: Add Dense Layers on top of Frozen Convolutional Layers
9. CNN Model: CNN Model: Freeze Convolutional Layers
10. CNN Model: Plot
11. Keras Applications: preprocess_input, decode_predictions and get all layers' output
12. CNN Model: LeNet
13. Activation Maximization using Keras Visualization Toolkit
14. How convolutional neural networks see the world(An exploration of convnet filters with Keras)
15. vgg16 1000 classes
16. MatPlotLib SubPlots, GridSpec, and Axes
17. Understanding your Convolution network with Visualizations
18. Data Augmentation : Show augmented image generated by ImageDataGenerator
19. Recall and Precision
20. Save model training history into file, load history from file
21. Plot loss charts and accuracy charts based on training history
22. Confusion Matrix and Classification Report
23. Find indexes of items which are not classified predicted correctly
24. Keras Callbacks
25. 【详解】keras + predict_generator + ImageDataGenerator.flow_from_ditectory 进行预测
26. Get classification class names and labels from validation_generator or train_generator
27. Confusion Matrix and Classification Report
28. model.evaluate 和 model.predict 的区别
29. model.predict_classes(test) 和model.predict(test) 区别