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artificial neuron - by Mcculloch and Pitts 1943
Perceptrons 感知器 by Rosenblatt 1958
backpropagation 反向传播 1974
feedforward
deep feedforward neural networks - modern techniques
Stee…
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This is an interesting stochastic optimizer with some nice theoretical guarantees for convex problems. Would be interesting to compare to the others we have implemented already.
https://papers.nips.c…
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I can see that Stochastic Gradient Descent has already been implemented. But linear regression works using simple gradient descent. What are the challenges to implementing SGD for Linear Regression.
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https://liam.page/2019/06/18/OCD-needs-stochastic-gradient-descent/
作为一个强迫症(OCD)患者,曾经我一直对随机梯度下降(Stochastic Gradient Descent)表示怀疑。毕竟,每次只选择少量样本计算梯度,这靠谱吗?强迫症患者心里泛起了浓浓的怀疑。然而经年的实践经验结合理论分析表明,强迫症患者也需要随机梯度…
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### Idea Contribution
- [X] I have read all the feature request issues.
- [X] I'm interested in working on this issue
- [X] I'm part of GSSOC organization
### Explain feature request
Deep Learning …
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@kangk9908
https://github.com/kcarnold/cs344-exam-23sp/blob/6a5024bc438f6db811ce74682ee7ba1fc4684112/u02-sa-learning-rate/SLO.md?plain=1#L1
From how I read the SLO from unit 2, I think it more rel…
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Some questions and suggestions came to mind when I read about the gradient descent method:
- In section [Gradient Descent](https://ml-course.github.io/master/notebooks/02%20-%20Linear%20Models.html…
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Motivation: While stochastic gradient descent algorithm has tolerance to the noise coming from monte carlo estimation of gradient, the noise from estimation of EI has great impact to the performance o…
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**Algorithms**
* Linear Model
- [x] Ordinary Least Squared Linear Regression
- [x] Gradient Descent Linear Regression
- [x] Stochastic Gradient Descent Linear Regression
- [x] Logist…
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Hi,
Thanks for releasing the code for active-qa.
After browsing the code, I did not find Monte-Carlo Sampling in the training stage. It seems that each training instance consists of only one 「…