scratchai | Deep Learning library that aims to store all Deep Learning | Machine Learning library

by iArunava Python Version: Current License: MIT

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

kandi X-RAY | scratchai Summary

scratchai is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Tensorflow applications. scratchai has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.

scratchai is a Deep Learning library that aims to store all Deep Learning algorithms. With easy calls to do all the common tasks in AI.

Support

Quality

Security

License

Reuse

Support

scratchai has a low active ecosystem.

It has 91 star(s) with 17 fork(s). There are 9 watchers for this library.

It had no major release in the last 6 months.

There are 8 open issues and 4 have been closed. On average issues are closed in 5 days. There are 20 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of scratchai is current.

Quality

scratchai has 0 bugs and 0 code smells.

Security

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

scratchai code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

scratchai is licensed under the MIT License. This license is Permissive.

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

Reuse

scratchai 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.

scratchai saves you 1730 person hours of effort in developing the same functionality from scratch.

It has 3830 lines of code, 256 functions and 56 files.

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

Top functions reviewed by kandi - BETA

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

Performs custom custom fitting
Adjust learning rate based on option group
Fit a lf - fit
Evaluate the test accuracy
Transfer weights from net
Updates the values
Transfer weights from sd to sd
Mask the given image with given parameters
Mark a list of pnts on an image
Forward computation
Construct a ResNet50 network
Construct a Resnet 34
Train the model
VGG network
VGG11 network
VGG16
Vgg model
VGG19 network
Construct a ResNet101 network
Generate a google layer
Fit the model
Plot a batch
Create a ResNet152
Plot a single batch of images
Return the standard deviation of t
Train the clf

Get all kandi verified functions for this library.

scratchai Key Features

No Key Features are available at this moment for scratchai.

scratchai Examples and Code Snippets

No Code Snippets are available at this moment for scratchai.

Community Discussions

Trending Discussions on scratchai

Pip installation in Colab with URL and requirements

QUESTION

Pip installation in Colab with URL and requirements

Asked 2021-Oct-06 at 06:58

I would like to install the following package in COLAB with its requirements. I would like to run this:

...

ANSWER

Answered 2021-Oct-06 at 06:58

!pip install scratchai-nightly -r https://raw.githubusercontent.com/iArunava/scratchai/master/requirements.txt

Try this, because this page only contains requirements and it doesn't have any HTML data, and https://github.com/iArunava/scratchai/blob/master/requirements.txt this you tried to install, has HTML data so, pip install tried to download HTML stuff like which doesn't exist in pip.

next time you are trying to get something from GitHub, click the raw button. https://prnt.sc/1uzm8ht

Source https://stackoverflow.com/questions/69460947

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

Vulnerabilities

No vulnerabilities reported

Install scratchai

You can download it from GitHub.
You can use scratchai 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.