Notes on "Theories on Deep Learning" (Stats 385)

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AndrewNg's CNN notes(practical issues)

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In this example, the author adapted the architecture obtained from training ILSVRC-2011 dataset which consists of 1000 classes with approximately 1.5 million images. And using the pre-trained VGG-16 network, the author fine-tuned the network on PASCAL VOC detection data (20 object classes, and 1 background class) and replaced the original 1000 way classification layer by a randomly initialized 21-way classification layer. Apart from that, the author keep the rest of the CNN architecture remains unchanged. After training of CNN parameters and filter the region proposals based on their IoU value, at the test stage, RCNN uses Selective Search to extract ~300 boxes that likely contain objects and evaluates the ConvNet on each one of them, followed by NMS(non-maximum suppression) within each class.
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Confidence intervals & Credible intervals

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https://stats.stackexchange.com/questions/2272/whats-the-difference-between-a-confidence-interval-and-a-credible-interval user28:  My understanding is as follows: Background Suppose that you have some data  x x  and you are trying to estimate  θ θ . You have a data generating process that describes how  x x  is generated conditional on  θ θ . In other words you know the distribution of  x x  (say,  f ( x | θ ) f ( x | θ ) . Inference Problem Your inference problem is: What values of  θ θ  are reasonable given the observed data  x x  ? Confidence Intervals Confidence intervals are a classical answer to the above problem. In this approach, you assume that there is  true, fixed  value of  θ θ . Given this assumption, you use the data  x x  to get to an estimate of  θ θ (say,  θ ^ θ ^ ). Once you have your estimate you want to assess where the true value is in relation t...
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