- Sep 2018
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192.168.199.102:5000 192.168.199.102:5000
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生成模型 vs. 判别模型
总体来看，如果样本足够多，判别模型的正确率高于生成模型的正确率。
生成模型和判别模型最大的区别在于，生成模型预先假设了很多东西，比如预先假设数据来自高斯，伯努利，符合朴素贝叶斯等等，相当于预先假设了 Hypothesis 函数集，只有在此基础上才有可能求出这个概率分布的参数。
生成模型，进行了大量脑补。脑补听起来并不是一件好事，但是当你的数据量太小的时候，则必须要求你的模型具备一定的脑补能力。
判别模型非常依赖样本，他就是很传统，死板，而生成模型比较有想象力，可以“想象”出不存在于当前样本集中的样本，所以他不那么依赖样本。
关于 想象出不能存在于当前样本集的样本 ，见本课程 40:00 老师举例。
生成模型在如下情形比判别模型好：
- 数据量较小时。
- 数据是noisy，标签存在noisy。
- 先验概率和类别相关的概率可以统计自不同的来源。
释疑第三条优点：老师举例，在语音辨识问题中，语音辨识部分虽然是 DNN --- 一个判别模型，但其整体确实一个生成模型，DNN 只是其中一块而已。为什么会这样呢？因为你还是要去算一个先验概率 --- 某一句话被说出来的概率，而获得这个概率并不需要样本一定是声音，只要去网络上爬很多文字对话，就可以估算出这个概率。只有 类别相关的概率 才需要声音和文字pair，才需要判别模型 --- DNN 出马。
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Annotators
URL
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- Nov 2017
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genomebiology.biomedcentral.com genomebiology.biomedcentral.com
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MCC
Matthews correlation coefficient
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- Oct 2017
- Jun 2017
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offconvex.github.io offconvex.github.io
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One explanation of Non-convex optimization
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Annotators
URL
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- May 2017
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www.analyticsvidhya.com www.analyticsvidhya.com
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Precision: It is a measure of correctness achieved in positive prediction i.e. of observations labeled as positive, how many are actually labeled positive. Precision = TP / (TP + FP) Recall: It is a measure of actual observations which are labeled (predicted) correctly i.e. how many observations of positive class are labeled correctly. It is also known as ‘Sensitivity’. Recall = TP / (TP + FN)
Example: In cancer research you may want higher recall, Since you want all actual positive observations to classified as True Positive. A lower Precision maybe alright because some healthy people classified as cancerous can be rectified later.
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- Apr 2017
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demo.clab.cs.cmu.edu demo.clab.cs.cmu.edu
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if your goal is word representation learning,you should consider both NCE and negative sampling
Wonder if anyone has compared these two approaches
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- Feb 2017
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sebastianraschka.com sebastianraschka.com
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Feature scaling in depth tutorial
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- Dec 2015
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cs231n.stanford.edu cs231n.stanford.edu
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some of the deep learning libraries we may look at later in the class
what c++ libraries are used?
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- Apr 2015
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arxiv.org arxiv.org
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Same as above but the pre- trained vectors are fine-tuned for each task.
How?
Backpropagating to the input layer, changing the vector representation with the training examples?
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- Sep 2014
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sites.psu.edu sites.psu.edu
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you
us
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you
we
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your
our
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your
our
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your
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ethical challenges.
the ethical challenges you will come up against along the way.
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