machine_learning_examples | A collection of machine learning examples and tutorials | Machine Learning library

by lazyprogrammer Python Version: Current License: No License

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

kandi X-RAY | machine_learning_examples Summary

machine_learning_examples is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow applications. machine_learning_examples has no vulnerabilities and it has medium support. However machine_learning_examples has 6 bugs and it build file is not available. You can download it from GitHub.

A collection of machine learning examples and tutorials. Find associated tutorials at Find associated courses at Please note that not all code from all courses will be found in this repository. Some newer code examples (e.g. everything from Tensorflow 2.0) were done in Google Colab. Therefore, you should check the instructions given in the lectures for the course you are taking.

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Quality

Security

License

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machine_learning_examples has a medium active ecosystem.

It has 7496 star(s) with 6080 fork(s). There are 615 watchers for this library.

It had no major release in the last 6 months.

There are 9 open issues and 38 have been closed. On average issues are closed in 810 days. There are 23 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of machine_learning_examples is current.

Quality

machine_learning_examples has 6 bugs (1 blocker, 0 critical, 5 major, 0 minor) and 2971 code smells.

Security

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

machine_learning_examples code analysis shows 0 unresolved vulnerabilities.

There are 9 security hotspots that need review.

License

machine_learning_examples 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

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

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

machine_learning_examples saves you 16489 person hours of effort in developing the same functionality from scratch.

It has 32787 lines of code, 1799 functions and 432 files.

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

Top functions reviewed by kandi - BETA

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

Train the model
Get the context indices from a sentence
Counts the number of words in the enwiki
Compute the negative sampling distribution of the negative words
Runs a test environment
Create the network
A convolutional layer
Estimate the cooccurrence matrix
Calculate momentum updates
Build the generator
Fit the autoencoders model
Get training data
Generate a windy grid
Get a list of sentences with word2id
Compute test accuracy
Compute training accuracy
Compute the Gaussian correlation matrix
Compute the decision tree
Calculate the cost function of the cost function
Get the wikipedia data
Compute the loss function
Connects the model
Compute the objective function
Play one step
Builds the model
Fit the model

Get all kandi verified functions for this library.

machine_learning_examples Key Features

No Key Features are available at this moment for machine_learning_examples.

machine_learning_examples Examples and Code Snippets

No Code Snippets are available at this moment for machine_learning_examples.

Community Discussions

Trending Discussions on machine_learning_examples

Forward propagation slow - Training time normal

QUESTION

Forward propagation slow - Training time normal

Asked 2018-Apr-16 at 21:13

I'm having trouble figuring out why when I perform forward propagation my code is extremely slow. The code in question can be found here: https://github.com/rekkit/lazy_programmer_ml_course/blob/develop/05_unsupervised_deep_learning/poetry_generator_rnn.py

I'm comparing the performance of my code to that of this: https://github.com/lazyprogrammer/machine_learning_examples/blob/master/rnn_class/srn_language_tf.py

The difference is when I run

...

ANSWER

Answered 2018-Apr-16 at 21:13

I've figured it out. Every time I call the predict method I'm rebuilding the graph. Instead, in the fit method I define a variable:

Source https://stackoverflow.com/questions/49865478

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

Vulnerabilities

No vulnerabilities reported

Install machine_learning_examples

You can download it from GitHub.
You can use machine_learning_examples 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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