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<!DOCTYPE html>
<html>
<link rel="shortcut icon" href="favicon.ico">
<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/4.7.0/css/font-awesome.min.css">
<meta name="author" content="sunprinceS" >
<meta property="og:image" content="joy.png"/>
<!--<meta property="og:image" content="http://www.themainstreetmouse.com/wp-content/uploads/2015/06/inhinh657551-613x345-150x150.jpg"/>-->
<title>Machine Learning (2017, Spring)</title>
<xmp theme="cerulean" style="display:none;">
# Assignment #3 - Image Sentiment Classification
### 重要事項宣佈
* 投影片連結 <a href="https://docs.google.com/presentation/d/1QFK4-inv2QJ9UhuiUtespP4nC5ZqfBjd_jP2O41fpTc/edit?ts=58e452ff#slide=id.p" target="_blank"><i class="fa fa-slideshare"></i></a>
* Kaggle 連結 <a href="https://inclass.kaggle.com/c/ml2017-hw3" target="_blank"><i class="fa fa-trophy"></i></a>
* Deadline: 2017/05/04 11:59 P.M. (GMT+8)
* TA會於4/20釋出範例程式碼，亦為超過 Kaggle simple baseline 的加分截止期限
* 在做 P4及P5時，請大家先看過這個關於 <a href="https://blog.keras.io/how-convolutional-neural-networks-see-the-world.html" target="_blank">visualization 的 tutorial</a>
<hr>
In this assignment, you will practice using Deep Learning libraries to experience the power of Neural Net.
The requirements of this assignment are as follows:
## <a href="./p1.html" target="_blank">P1: Build Convolution Neural Network (1%)</a>
*[Accuracy]* Build CNN model, and tune it to the best formance as possible as you can.
Record your model structure and training procedure.
## <a href="./p2.html" target="_blank">P2: Build Deep Neural Network (1%)</a>
*[Accuracy]* Using the same number of parameters as above CNN, build a DNN model to do this task.
Record your model structure and training procedure. Explain what you observed.
## <a href="./p3.html" target="_blank">P3: Analyze the Model by Confusion Matrix (1%)</a>
*[Analysis]* Observe the prediction of your validation data( 10% ~ 20% of training data is OK ).
Plot the prediction into confusion matrix and describe what you observed.
## <a href="./p4.html" target="_blank">P4: Analyze the Model by Plotting the Saliency Map (1%)</a>
*[Analysis]* Plot the saliency map of original image to see which part is important when classifying
## <a href="./p5.html" target="_blank">P5: Analyze the Model by Visualizing Filters (1%)</a>
*[Analysis]* Use Gradient Ascent method mentioned in class to find the image that activates the selected filter the most and plot them.
## <a href="./bonus.html" target="_blank">Bonus: Semi-supervised Learning (1%)</a>
You can split part of training data and remove their label.
Then try semi-supervised learning techniques (self-training, clustering...) taught in class, and record its performance.
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<center><i class="fa fa-github"></i></a> Posted by: <a href="https://github.com/sunprinceS/" target="_blank">sunprinceS</a> </center>
<center><i class="fa fa-envelope"></i> Contact information: <a href="mailto:"> [email protected] </a>.</center>
<center><i class="fa fa-mortar-board"></i> Course information: <a href="http://speech.ee.ntu.edu.tw/~tlkagk/courses_ML17.html", target="_blank">Machine Learning (2017, Spring) @ National Taiwan University</a>.</center>
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