sebastianruder | Repository for my personal website

by sebastianruder HTML Version: Current License: No License

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kandi X-RAY | sebastianruder Summary

sebastianruder is a HTML library. sebastianruder has no bugs, it has no vulnerabilities and it has low support. You can download it from GitHub.

Repository for my personal website

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sebastianruder has a low active ecosystem.

It has 34 star(s) with 7 fork(s). There are 5 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 0 have been closed. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of sebastianruder is current.

Quality

sebastianruder has no bugs reported.

Security

sebastianruder has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

sebastianruder does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

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sebastianruder releases are not available. You will need to build from source code and install.

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sebastianruder Key Features

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Community Discussions

Trending Discussions on sebastianruder

Implementing Adagrad in Python

Why is RMSProp considered "leaky"?

Knowledge transfer in regularised linear regression

QUESTION

Implementing Adagrad in Python

Asked 2018-May-03 at 05:56

I'm trying to implement Adagrad in Python. For learning purposes, I am using matrix factorisation as an example. I'd be using Autograd for computing the gradients.

My main question is if the implementation is fine.

Problem description

Given a matrix A (M x N) having some missing entries, decompose into W and H having sizes (M x k) and (k X N) respectively. Goal would to learn W and H using Adagrad. I'd be following this guide for the Autograd implementation.

NB: I very well know that ALS based implementation are well-suited. I'm using Adagrad only for learning purposes

Customary imports ...

ANSWER

Answered 2017-Jun-15 at 15:48

At a cursory glance, your code closely matches that at https://github.com/benbo/adagrad/blob/master/adagrad.py

Source https://stackoverflow.com/questions/44405297

QUESTION

Why is RMSProp considered "leaky"?

Asked 2017-Jul-05 at 22:35

decay_rate = 0.99 # decay factor for RMSProp leaky sum of grad^2

...

ANSWER

Answered 2017-Jul-05 at 22:35

RMsprop keeps the exponentialy decaying average of squared gradients. Wording (however unfortunate) of "leaky" refers to the fact how much of the previous estimate "leaks" to the current one, since

Source https://stackoverflow.com/questions/44936983

QUESTION

Knowledge transfer in regularised linear regression

Asked 2017-Jan-27 at 13:03

By default all regularised linear regression techniques of scikit-learn pull the model coefficients w towards 0 with increased alpha. Is it possible to instead pull the coefficients towards some predefined values? In my application I do have such values that have been obtained from a previous analysis of a similar but much larger dataset. In other words, can I transfer the knowledge from one model to another?

The documentation of LassoCV says:

The optimization objective for Lasso is:
...

ANSWER

Answered 2017-Jan-24 at 20:35

In short - yes, you need to do it by hand by recompiling everything. Scikit-learn is not a library for customizable ML models. It is about providing simple, typical models with easy to use interface. If you want customization you should look for things like tensorflow, keras etc. or at least - autograd. In fact with autograd this is extremely simple, since you can write your code with numpy and use autograd to compute gradients.

Source https://stackoverflow.com/questions/41838056

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

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