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7/8 Optimization 방법론
- Optimization 방법론의 발전
- Gradient Descent Algorithm
- 어떠한 함수의 최소점을 찾는것
- 함수의 공간은 파라미터, 파라미터 갯수가 엄청나게 늘어나면 함수 형태 파악 불가능
- 파라미터의 기울기만을 알고 있다고 가정(코스트 함수를 최소화 하기 위해, 코스…
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We can compare to this JMLR paper : [Stochastic Gradient Descent as Approximate Bayesian Inference](https://arxiv.org/abs/1704.04289).
They have experiments which are easier to work with : Linear Reg…
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## 🚀 Feature
I would like to suggest new stochastic optimizer additions to Pytorch.
### For non-convex loss functions
It is known that adaptive stochastic optimizers like Adam, Adagrad, RMSprop c…
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This issue can be a collection and discussion of methods we could add to the library at some point, in no particular order :) Feel free to comment with suggestions and if you feel comfortable, you are…
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Hi Angus, have you ever considered fitting the GMM through a gradient based method instead of EM, such as mentioned here: https://stats.stackexchange.com/questions/64193/fitting-a-gaussian-mixture-mod…
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How can we change the optimizer in Gluonts instead of stochastic gradient descent ? Suppose I want to use some evolutionary optimizer instead of SGD , how can I implement that in gluonts ??
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Most of methods in the list will be implemented in the order.
- inference for Sparse Gaussian process regression (based on JMLR 2005 "A unifying view of sparse approximate Gaussian process regression…
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Hi,
I have some questions about the programming assignment.
Q1) What kind of dataset do we have to use?
- In order to implement the machine learning algorithm, we should have a specific goal.
…
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Let me clarify the confusion on the orthogonal directions obtained with the optimal alpha in slide 66/127 of the lecture "Stochastic gradient descent".
Please read this while looking at the slide.
…
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Hi Kris,
When I ran the SageMaker notebook for Stochastic Gradient Descent, I came across a stacktrace for the `ani` output on the last line. The notebook didn't have ffmpeg installed, so I had to …