SSMSE(Microsoft SQL Server Management Studio Express)는 SQL Server 2005 Express Edition 및 SQL Server 2005 Express Edition with Advanced Services를 관리할 수 있는 사용이 용이한 무료 그래픽 관리 도구입니다.

63비트 다운로드 후 관리자권한 으로 실행. 

https://www.microsoft.com/ko-kr/download/details.aspx?id=8961

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Galaxy S8 | S8+  인피니티 디스플레이

http://www.samsung.com/global/galaxy/galaxy-s8/intelligence/

Inspired by nature, the Galaxy S8’s gentle surface screen forms a continuous silhouette with no protrusions or sharp angles, while the phone’s subtle curvature smoothly flows from the front to back with symmetrical balance. 

For more information about Galaxy S8 design, read: http://bit.ly/2oxAyCG

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데이터 저널리즘을 돕는 구글 도구 7선

TensorFlow-Tutorials

Introduction to deep learning based on Google's TensorFlow framework. These tutorials are direct ports of Newmu's Theano Tutorials.

Topics

• Simple Multiplication
• Linear Regression
• Logistic Regression
• Feedforward Neural Network (Multilayer Perceptron)
• Deep Feedforward Neural Network (Multilayer Perceptron with 2 Hidden Layers O.o)
• Convolutional Neural Network
• Denoising Autoencoder
• Recurrent Neural Network (LSTM)
• Word2vec
• TensorBoard
• Save and restore net
• Generative Adversarial Network

Dependencies

• TensorFlow 1.0 alpha
• Numpy
• matplotlib

# TensorFlow-Tutorials

[](https://travis-ci.org/nlintz/TensorFlow-Tutorials)

[](https://www.codacy.com/app/hunkim/TensorFlow-Tutorials)

Introduction to deep learning based on Google's TensorFlow framework. These tutorials are direct ports of

Newmu's [Theano Tutorials](https://github.com/Newmu/Theano-Tutorials).

***Topics***

* [Simple Multiplication](00_multiply.py)

* [Linear Regression](01_linear_regression.py)

* [Logistic Regression](02_logistic_regression.py)

* [Feedforward Neural Network (Multilayer Perceptron)](03_net.py)

* [Deep Feedforward Neural Network (Multilayer Perceptron with 2 Hidden Layers O.o)](04_modern_net.py)

* [Convolutional Neural Network](05_convolutional_net.py)

* [Denoising Autoencoder](06_autoencoder.py)

* [Recurrent Neural Network (LSTM)](07_lstm.py)

* [Word2vec](08_word2vec.py)

* [TensorBoard](09_tensorboard.py)

* [Save and restore net](10_save_restore_net.py)

* [Generative Adversarial Network](11_gan.py)

***Dependencies***

* TensorFlow 1.0 alpha

* Numpy

* matplotlib

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https://www.tensorflow.org/api_docs/python/tf/compat

Module: tf.compat

scikit-learn: machine learning in Python http://scikit-learn.org

https://github.com/scikit-learn/scikit-learn

scikit-learn

scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.

The project was started in 2007 by David Cournapeau as a Google Summer of Code project, and since then many volunteers have contributed. See the AUTHORS.rst file for a complete list of contributors.

It is currently maintained by a team of volunteers.

Website: http://scikit-learn.org

Installation

Dependencies

scikit-learn requires:

• Python (>= 2.7 or >= 3.3)
• NumPy (>= 1.6.1)
• SciPy (>= 0.9)

For running the examples Matplotlib >= 1.1.1 is required.

scikit-learn also uses CBLAS, the C interface to the Basic Linear Algebra Subprograms library. scikit-learn comes with a reference implementation, but the system CBLAS will be detected by the build system and used if present. CBLAS exists in many implementations; see Linear algebra libraries for known issues.

User installation

If you already have a working installation of numpy and scipy, the easiest way to install scikit-learn is using pip

pip install -U scikit-learn

or conda:

conda install scikit-learn

The documentation includes more detailed installation instructions.

Development

We welcome new contributors of all experience levels. The scikit-learn community goals are to be helpful, welcoming, and effective. The Development Guide has detailed information about contributing code, documentation, tests, and more. We've included some basic information in this README.

Important links

• Official source code repo: https://github.com/scikit-learn/scikit-learn
• Download releases: https://pypi.python.org/pypi/scikit-learn
• Issue tracker: https://github.com/scikit-learn/scikit-learn/issues

Source code

You can check the latest sources with the command:

git clone https://github.com/scikit-learn/scikit-learn.git

Setting up a development environment

Quick tutorial on how to go about setting up your environment to contribute to scikit-learn: https://github.com/scikit-learn/scikit-learn/blob/master/CONTRIBUTING.md

Testing

After installation, you can launch the test suite from outside the source directory (you will need to have the nose package installed):

nosetests -v sklearn

Under Windows, it is recommended to use the following command (adjust the path to the python.exe program) as using the nosetests.exe program can badly interact with tests that use multiprocessing:

C:\Python34\python.exe -c "import nose; nose.main()" -v sklearn

See the web page http://scikit-learn.org/stable/developers/advanced_installation.html#testing for more information.

Random number generation can be controlled during testing by setting the SKLEARN_SEED environment variable.

Submitting a Pull Request

Before opening a Pull Request, have a look at the full Contributing page to make sure your code complies with our guidelines: http://scikit-learn.org/stable/developers/index.html

Project History

The project was started in 2007 by David Cournapeau as a Google Summer of Code project, and since then many volunteers have contributed. See the AUTHORS.rst file for a complete list of contributors.

The project is currently maintained by a team of volunteers.

Note: scikit-learn was previously referred to as scikits.learn.

Help and Support

Documentation

• HTML documentation (stable release): http://scikit-learn.org
• HTML documentation (development version): http://scikit-learn.org/dev/
• FAQ: http://scikit-learn.org/stable/faq.html

Communication

• Mailing list: https://mail.python.org/mailman/listinfo/scikit-learn
• IRC channel: #scikit-learn at webchat.freenode.net
• Stack Overflow: http://stackoverflow.com/questions/tagged/scikit-learn
• Website: http://scikit-learn.org

Citation

If you use scikit-learn in a scientific publication, we would appreciate citations: http://scikit-learn.org/stable/about.html#citing-scikit-learn

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