MLclass | Deep Learning Zoo '' course at UC Davis | Machine Learning library

by pipidog Python Version: Current License: No License

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

kandi X-RAY | MLclass Summary

MLclass is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Tensorflow applications. MLclass has no bugs, it has no vulnerabilities and it has low support. However MLclass build file is not available. You can download it from GitHub.

This is the repo of my "Deep Learning Zoo" course at UC Davis

Support

Quality

Security

License

Reuse

Support

MLclass has a low active ecosystem.

It has 8 star(s) with 3 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

MLclass has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of MLclass is current.

Quality

MLclass has no bugs reported.

Security

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

License

MLclass 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

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

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

Top functions reviewed by kandi - BETA

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

Generate white noise image
Deprocessing image
Create argument parser
Read all files of a given type
Probe a time series of data
Compute the total variation loss of the image
Plot images and prediction
Plots the image
Adds a layer
Calculate style reconstruction loss
Computes the gram matrix
Show info about the model
Plot a function with labels and labels
Plot the loss of the generator
Generate a label for peer training
Plot training and validation history
Train the discriminator
Calculate the style loss of a style combination
Creates a fake image
Compute accuracy
Preprocess an image file
Evaluate the loss function
Compute loss and gradients

Get all kandi verified functions for this library.

MLclass Key Features

No Key Features are available at this moment for MLclass.

MLclass Examples and Code Snippets

No Code Snippets are available at this moment for MLclass.

Community Discussions

Trending Discussions on MLclass

Andrew Ng's ML course (in python) - Applying gradient descent with multiple variables, confused about intuition

QUESTION

Andrew Ng's ML course (in python) - Applying gradient descent with multiple variables, confused about intuition

Asked 2020-Jun-30 at 20:20

I am trying to create the equation for gradient descent with multiple variables. Picture of equation: https://www.holehouse.org/mlclass/04_Linear_Regression_with_multiple_variables_files/Image%20[3].png

The final solution is:

...

ANSWER

Answered 2020-Jun-30 at 19:30

np.dot includes the summation of multiplied elements.

Source https://stackoverflow.com/questions/62662375

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

Vulnerabilities

No vulnerabilities reported

Install MLclass

You can download it from GitHub.
You can use MLclass 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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