Yaae | Yaae : Yet another autodiff engine | Machine Learning library
kandi X-RAY | Yaae Summary
kandi X-RAY | Yaae Summary
Yaae is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Numpy applications. Yaae has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can download it from GitHub.
Yaae (Yet another autodiff engine) is an automatic differentiation engine written in Numpy which comes with a small neural networks library. It supports scalar operations as well as tensors operations and comes with various functions such as exponential, relu, sigmoid ... For educational puprose only. Here is my blog post explaining how an automatic differentiation works and my design/implementation choices.
Yaae (Yet another autodiff engine) is an automatic differentiation engine written in Numpy which comes with a small neural networks library. It supports scalar operations as well as tensors operations and comes with various functions such as exponential, relu, sigmoid ... For educational puprose only. Here is my blog post explaining how an automatic differentiation works and my design/implementation choices.
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Support
Yaae has a low active ecosystem.
It has 16 star(s) with 0 fork(s). There are 1 watchers for this library.
It had no major release in the last 6 months.
Yaae has no issues reported. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of Yaae is current.
Quality
Yaae has 0 bugs and 0 code smells.
Security
Yaae has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
Yaae code analysis shows 0 unresolved vulnerabilities.
There are 0 security hotspots that need review.
License
Yaae 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.
Reuse
Yaae releases are not available. You will need to build from source code and install.
Build file is available. You can build the component from source.
Installation instructions, examples and code snippets are available.
Yaae saves you 268 person hours of effort in developing the same functionality from scratch.
It has 650 lines of code, 84 functions and 5 files.
It has medium code complexity. Code complexity directly impacts maintainability of the code.
Top functions reviewed by kandi - BETA
kandi has reviewed Yaae and discovered the below as its top functions. This is intended to give you an instant insight into Yaae implemented functionality, and help decide if they suit your requirements.
- Compute the derivative of the node
- Compress the gradient of the given tensor
- Sum over each axis
- Forward pass through the sum of nodes
- Computes the gradient of the node
- Compute the gradient of the node
Get all kandi verified functions for this library.
Yaae Key Features
No Key Features are available at this moment for Yaae.
Yaae Examples and Code Snippets
No Code Snippets are available at this moment for Yaae.
Community Discussions
Trending Discussions on Yaae
QUESTION
Pasting values from different columns and different rows into a new df in R
Asked 2019-Mar-20 at 03:31
I have the following df below:
...ANSWER
Answered 2019-Mar-20 at 01:32In Base R , We using tail head and cumsum create the group key , then using aggregate
QUESTION
grunt-contrib-cssmin: css source map appears empty in dev tools
Asked 2017-Oct-06 at 12:48
I am using grunt with cssmin to minify and concatenate css files.
Css files are well concatenated and minified but when I try to watch the non minified files in chrome dev tools under 'Sources' tab, files appears empty.
Here is my cssmin task:
ANSWER
Answered 2017-Oct-05 at 13:25Edit: Firstly, try specifying a root in your options of the Gruntfile.js as follows:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install Yaae
Let's compare a simple example using Yaae and Pytorch.
Create a virtual environment in the root folder using virtualenv and activate it.
Install requirements.txt.
Create a virtual environment in the root folder using virtualenv and activate it.
Install requirements.txt.
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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