probabilistic-models | probabilistic models and inference algorithms | Machine Learning library

by wiseodd Python Version: Current License: BSD-3-Clause

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | probabilistic-models Summary

probabilistic-models is a Python library typically used in Artificial Intelligence, Machine Learning applications. probabilistic-models has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However probabilistic-models build file is not available. You can download it from GitHub.

Collection of examples of various probabilistic models and inference algorithms.

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probabilistic-models has a low active ecosystem.

It has 220 star(s) with 71 fork(s). There are 15 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 0 have been closed. On average issues are closed in 558 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of probabilistic-models is current.

Quality

probabilistic-models has 0 bugs and 7 code smells.

Security

probabilistic-models has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

probabilistic-models code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

probabilistic-models is licensed under the BSD-3-Clause License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

probabilistic-models releases are not available. You will need to build from source code and install.

probabilistic-models has no build file. You will be need to create the build yourself to build the component from source.

probabilistic-models saves you 285 person hours of effort in developing the same functionality from scratch.

It has 689 lines of code, 5 functions and 17 files.

It has low code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed probabilistic-models and discovered the below as its top functions. This is intended to give you an instant insight into probabilistic-models implemented functionality, and help decide if they suit your requirements.

Gaussian kernel function
1D Gaussian kernel
Compute the standard deviation of Dirichlet distribution
Compute the direction of the gaussian distribution
2D Gaussian kernel
Compute softmax of x

Get all kandi verified functions for this library.

probabilistic-models Key Features

No Key Features are available at this moment for probabilistic-models.

probabilistic-models Examples and Code Snippets

No Code Snippets are available at this moment for probabilistic-models.

Community Discussions

Trending Discussions on probabilistic-models

In CBOW model, do we need to take Average at Hidden layer?

QUESTION

In CBOW model, do we need to take Average at Hidden layer?

Asked 2020-Sep-25 at 00:47

I search and read some articles about CBOW. But seem to have difference between these articles.

As I understand:

Input is a batch vector. And we will feed it to Hidden layer. So that we will get another batch vector H at Hidden layer.
In an article (part 2.2.1), they say that we will not use any Activation Function at Hidden layer, but we will take average on batch vector H to get a single vector (not a batch anymore). Then we will feed this average vector to Output layer and apply Softmax on it.

However, in this Coursera's video, they don't take average on batch vector H. They just feed this batch vector H to Output layer and apply Softmax on batch Output vector. And then calculate Cost function on it.
And, in Coursera's video, they say that we can use RelU as Activation function at Hidden layer. Is this a new method? Because I read many articles, but they always say that there is no Activation function at Hidden layer.

Can you please help me to answer it?

...

ANSWER

Answered 2020-Sep-25 at 00:47

In actual implementations – whose source code you can review – the set of context-word vectors are averaged together before being fed as the "input" to the neural-network.

Then, any back-propagated adjustments to the input are also applied to all the vectors contributing to that average.

(For example, in the original word2vec.c released with Google's original word2vec paper, you can see the tallying of vectors into neu1, then averaging via division by the context-window count cw, at:

https://github.com/tmikolov/word2vec/blob/master/word2vec.c#L444-L448 )

Source https://stackoverflow.com/questions/64053985

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

Vulnerabilities

No vulnerabilities reported

Install probabilistic-models

You can download it from GitHub.
You can use probabilistic-models like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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