DeepCoNN | This is our implementation of DeepCoNN | Machine Learning library
kandi X-RAY | DeepCoNN Summary
kandi X-RAY | DeepCoNN Summary
DeepCoNN is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow applications. DeepCoNN has no bugs, it has no vulnerabilities and it has low support. However DeepCoNN build file is not available. You can download it from GitHub.
This is our implementation for the paper:. 1、DeepCoNN: This is the state-of-the-art method that uti-lizes deep learning technology to jointly model user and itemfrom textual reviews. 2、DeepCoNN++: We extend DeepCoNN by changing its share layer from FM to our neural prediction layer.
This is our implementation for the paper:. 1、DeepCoNN: This is the state-of-the-art method that uti-lizes deep learning technology to jointly model user and itemfrom textual reviews. 2、DeepCoNN++: We extend DeepCoNN by changing its share layer from FM to our neural prediction layer.
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DeepCoNN has a low active ecosystem.
It has 145 star(s) with 64 fork(s). There are 7 watchers for this library.
It had no major release in the last 6 months.
There are 15 open issues and 5 have been closed. On average issues are closed in 3 days. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of DeepCoNN is current.
Quality
DeepCoNN has 0 bugs and 0 code smells.
Security
DeepCoNN has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
DeepCoNN code analysis shows 0 unresolved vulnerabilities.
There are 0 security hotspots that need review.
License
DeepCoNN 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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DeepCoNN releases are not available. You will need to build from source code and install.
DeepCoNN has no build file. You will be need to create the build yourself to build the component from source.
Installation instructions are not available. Examples and code snippets are available.
DeepCoNN saves you 374 person hours of effort in developing the same functionality from scratch.
It has 891 lines of code, 16 functions and 6 files.
It has high code complexity. Code complexity directly impacts maintainability of the code.
Top functions reviewed by kandi - BETA
kandi has reviewed DeepCoNN and discovered the below as its top functions. This is intended to give you an instant insight into DeepCoNN implemented functionality, and help decide if they suit your requirements.
- Initialize the model .
- Load training data .
- Prepare and preprocess the data .
- Pads a list of sentences to a given length .
- Build the vocabulary .
- Run dev step .
- Clean a string .
- Train a single training step .
- Return batches of data .
- Build input data .
Get all kandi verified functions for this library.
DeepCoNN Key Features
No Key Features are available at this moment for DeepCoNN.
DeepCoNN Examples and Code Snippets
No Code Snippets are available at this moment for DeepCoNN.
Community Discussions
Trending Discussions on DeepCoNN
QUESTION
How to transfer the following tensorflow code into pytorch
Asked 2019-Mar-21 at 16:11
I want to re-implement the word embedding here
here is the original tensorflow code (version: 0.12.1)
...ANSWER
Answered 2019-Mar-15 at 23:16The pytorch equivalent of the tensorflow part of the code will be, explained with comments in the code itself, you have to import truncnorm from scipy.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install DeepCoNN
You can download it from GitHub.
You can use DeepCoNN 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.
You can use DeepCoNN 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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