SciPy2015 | Talk for SciPy2015 Deep Learning : Tips From The Road | Machine Learning library

by kastnerkyle Python Version: Current License: BSD-3-Clause

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kandi X-RAY | SciPy2015 Summary

SciPy2015 is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning applications. SciPy2015 has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However SciPy2015 build file is not available. You can download it from GitHub.

PDF version of slides included. Slides also uploaded to Video of talk here: To run the code in the graphics directory, you will need Theano. I have not run this on CPU yet, but it runs pretty quickly on GPU. To run the code, simply go to graphics_code/vae or graphics_code/cvae. will start training the model. After training,. will generate plots for the saved model.

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SciPy2015 has a low active ecosystem.

It has 43 star(s) with 11 fork(s). There are 7 watchers for this library.

It had no major release in the last 6 months.

SciPy2015 has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of SciPy2015 is current.

Quality

SciPy2015 has 0 bugs and 0 code smells.

Security

SciPy2015 has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

SciPy2015 code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

SciPy2015 is licensed under the BSD-3-Clause License. This license is Permissive.

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

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SciPy2015 releases are not available. You will need to build from source code and install.

SciPy2015 has no build file. You will be need to create the build yourself to build the component from source.

SciPy2015 saves you 1327 person hours of effort in developing the same functionality from scratch.

It has 2976 lines of code, 283 functions and 7 files.

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

Top functions reviewed by kandi - BETA

kandi has reviewed SciPy2015 and discovered the below as its top functions. This is intended to give you an instant insight into SciPy2015 implemented functionality, and help decide if they suit your requirements.

Fetch the MNIST dataset
Download a file
Get dataset directory
Download the MNIST dataset
Iterate over the given function
Computes minibatch indices from an iterable
Wrapper function for early stopping
Wrapper function for checkpoint_status
Add datasets to graph
Create a new name for a given shape
Generate a path between two points
Crossplus layer
Generate samples
Calculates the Gaussian logarithm
Calculate the Gaussian KL divergence
Wrap a switch function
R Projection layer
Dropout layer
Fetch the lovecraft file
Return a list of all UCI words
Generate a function for text minibatch
Gaussian log layer
Performs theano - repeat of an array
Compute updates for given parameters
Gaussian sampling layer
Create a softmax sampling layer

Get all kandi verified functions for this library.

SciPy2015 Key Features

No Key Features are available at this moment for SciPy2015.

SciPy2015 Examples and Code Snippets

No Code Snippets are available at this moment for SciPy2015.

Community Discussions

Trending Discussions on SciPy2015

Viewing dask graph from a dask object

QUESTION

Viewing dask graph from a dask object

Asked 2017-Nov-20 at 21:57

In Matthew Rocklin's presentation on Dask (2015), he shows an example for viewing the dask graph underlying a dask.array:

It seems that the representation of that property has changed a bit since:

...

ANSWER

Answered 2017-Nov-20 at 21:57

You can convert ShareDict objects into normal dict objects by calling dict on them.

Source https://stackoverflow.com/questions/47401262

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

Vulnerabilities

No vulnerabilities reported

Install SciPy2015

You can download it from GitHub.
You can use SciPy2015 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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