awsome-ML-DL-leaning | 李航《统计学习方法》笔记（带代码版） -
kandi X-RAY | awsome-ML-DL-leaning Summary
kandi X-RAY | awsome-ML-DL-leaning Summary
awsome-ML-DL-leaning is a Python library. awsome-ML-DL-leaning has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However awsome-ML-DL-leaning build file is not available. You can download it from GitHub.
awsome-ML-DL-leaning
awsome-ML-DL-leaning
Support
Quality
Security
License
Reuse
Support
awsome-ML-DL-leaning has a low active ecosystem.
It has 26 star(s) with 14 fork(s). There are 2 watchers for this library.
It had no major release in the last 6 months.
awsome-ML-DL-leaning has no issues reported. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of awsome-ML-DL-leaning is current.
Quality
awsome-ML-DL-leaning has 0 bugs and 0 code smells.
Security
awsome-ML-DL-leaning has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
awsome-ML-DL-leaning code analysis shows 0 unresolved vulnerabilities.
There are 0 security hotspots that need review.
License
awsome-ML-DL-leaning 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
awsome-ML-DL-leaning releases are not available. You will need to build from source code and install.
awsome-ML-DL-leaning has no build file. You will be need to create the build yourself to build the component from source.
awsome-ML-DL-leaning saves you 359 person hours of effort in developing the same functionality from scratch.
It has 857 lines of code, 82 functions and 13 files.
It has medium code complexity. Code complexity directly impacts maintainability of the code.
Top functions reviewed by kandi - BETA
kandi has reviewed awsome-ML-DL-leaning and discovered the below as its top functions. This is intended to give you an instant insight into awsome-ML-DL-leaning implemented functionality, and help decide if they suit your requirements.
- Fit the model
- Compare alpha
- Calculate the gradient of the Gaussian distribution
- Calculate the kernel function
- Compute the prediction score
- Predict the k nearest neighbors
- Compute the info gain
- Compute the condition entropy of a dataset
- Fit the logistic regression model
- Returns the data matrix
- Calculates the probability distribution
- Calculates the pmf function for a single parameter distribution
- Fit the model
- Preorder the tree
- Run GaussianNB
- Compute the accuracy of the test
- Compute the accuracy of the model
- Find the nearest node to the given point
- Fit the clustering
- Computes the score of the prediction
- Compute the KKT for a given parameter i e
- Computes the accuracy of the test
- Perform the backward algorithm
- This function is used to create the data
- Predict k nearest neighbors
- Forward function
- Compute the distance between two points
Get all kandi verified functions for this library.
awsome-ML-DL-leaning Key Features
No Key Features are available at this moment for awsome-ML-DL-leaning.
awsome-ML-DL-leaning Examples and Code Snippets
No Code Snippets are available at this moment for awsome-ML-DL-leaning.
Community Discussions
No Community Discussions are available at this moment for awsome-ML-DL-leaning.Refer to stack overflow page for discussions.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install awsome-ML-DL-leaning
You can download it from GitHub.
You can use awsome-ML-DL-leaning 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 awsome-ML-DL-leaning 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 .
Find more information at:
Reuse Trending Solutions
Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from over 650 million Knowledge Items
Find more librariesStay Updated
Subscribe to our newsletter for trending solutions and developer bootcamps
Share this Page
