Machine Learning Jeju Camp http://mlcampjeju.com/

GitHub -  https://github.com/TensorFlowKR/MLJejuCamp

Call for application for Machine Learning Camp Jeju 2017

If you have studied machine learning/deep learning and TensorFlow, you probably want to implement a non-trivial and large-scale system for real use. We invite you to the month-long Machine Learning Camp Jeju 2017, where you can make that dream a reality.

For a full month in beautiful Jeju Island, you and other participants will train a deep learning model using TensorFlow from start-to-finish. Jeff Dean (Google Senior Fellow via Hangout), Rajat Monga (Google/TensorFlow Director (TBC)), and Prof. KyungHyun Cho (NYU) will give us keynote talks. Plus, you will have access to experienced mentors including Namju Kim (Head of Research for Kakao Brain), Sung Kim (HKUST), Lucy Park (TF-KR), Donghyun Kwak (TF-KR), Terry Taewoong Um (TF-KR), and many more. We hope you take advantage of this wonderful opportunity.

Those selected as participants will be provided with one round-trip airfare (up to 300 USD) to Jeju Island (South Korea), room and board at Jeju National University, USD 1,000 in stipends (can be used for the airfare, etc.) and USD 500 to 1,000 in Google Cloud Credit. In addition to these benefits, participants will gain valuable and practical experience in the field of deep learning. We look forward to your application!

Mentor Recruitment: If you’re interested in sharing your experiences and expertise with the camp, please contact us at mljejucamp@googlegroups.com. You will serve as personal mentors to 1 to 2 participants, holding 2 to 3 on/offline meetings a week to help them successfully complete their projects. While it is possible for you to provide online-only mentoring, we suggest you visit Jeju Island to meet with your mentees in person. We will provide round-trip airfare (up to USD 300) to Jeju Island and up to five (5) days of room and board.

(Information regarding schedule, program and benefits are subject to change as we are in the process of finalizing the details. We will have more information later.)

Camp Overview

• Date: July 3rd through 28th, 2017 (Check-in date: July 2nd)
• Participants: 20
• Location: Jeju National University / Kakao Space.1
• Organizers: TensorFlow Korea User Group, Kakao, Google, Smart Grid CK Center in Jeju National Univ, Jeju Center for Creative Economy and Innovation, Jeju Local Government (Subject to change)
• Home page: https://github.com/TensorFlowKR/MLJejuCamp
• Application: https://www.surveymonkey.com/r/LY29GM5 (By April 20 11:59PM AOE)
• Contacts: Please leave your comments/questions on issues (https://github.com/TensorFlowKR/MLJejuCamp/issues) in this page.

Benefits (TBD)

• Full month of hands-on experience training deep learning models with TensorFlow and mentorship from top developers
• Round-trip airfare to Jeju Island (up to $300 USD)
• Accomodation in Jeju National University or Kakao Space, Jeju
• Stipend: 1,000 USD (can be used for the airfare, etc.)
• Google Cloud Credit ($500~1000 TBD)

Qualification

• No nationality, gender, age, degree, education requirements
• Must be able to stay in Jeju Island from July 3rd to 28th. (Weekday camp programs run from 10AM to 5PM)
• Good understanding of TensorFlow and deep learning and ability to train models (should be able to understand all in https://github.com/hunkim/DeepLearningZeroToAll)
• Being able to release the code written during the camp publicly on github
• Basic communication skills in English (All programs will be in English)

Application Closed! (By April 20 11:59PM AOE)

• Detailed proposal for Deep Learning Camp Jeju 2017 project (Please be as detailed as possible)
• CV that showcases applicant’s experience with deep learning and TensorFlow
• Previously implemented models (GitHub or other)
• Other supporting materials to show your qualification
• Application link (closed): https://www.surveymonkey.com/r/LY29GM5

Proposal examples:

• “I will implement paper X from 2016 NIPS Conference using TensorFlow and apply idea Y to the implementation”
• “My goal is to add idea X to existing TensorFlow model Y and apply it to dataset Z” (Please justify why you are interested in the particular paper, model, dataset, etc. Write your proposal as detailed as possible as it will be the primary criteria to select participants.)

Basic Tasks (but not limited to)

• Each participant will implement own deep learning related ideas and recently published ideas (in ICML, ICLR, NIPS, etc.) in TensorFlow. Or adapt already implemented ideas to new dataset. Participants will propose in the application.
• Participate in camp program. (10AM-5PM on weekday basis from July 1 to July 30)
• Deep learning and TensorFlow expert mentors will advise each participant.
• Release the code on the github as Open Source.

Camp rules

participants may be dismissed from the camp for the following or similar reasons:

• Repeatedly engage in behavior that negatively impacts other participants' work
• Spend unreasonable amount of time on non-camp related tasks.
• More than 3 missed camp days without proper notice.

Important dates

• Application due: April 20 (AOE time zone)
• Notification: May 10
• Mentor assignment and online discussion: June 1
• Camp starts: July 3

About Jeju

Located just off the coast of the Korean peninsula, Jeju Island is the largest volcanic island in Korea. Also known as Asia's Hawaii, the island is rife with beautiful sceneries and getaway resorts. Jeju boasts several natural treasures including Mount Halla, the country's highest peak, Trail Olle that winds around the rocky coastline, and Sunrise Peak, a dormant volcano ideal for catching sunrises and sunsets. You will be able to fully experience the island's charm for as long as a month without a visa. For more information, please visit at: http://www.visitjeju.net/en/index.jto.

About Camp

Deep Learning Camp Jeju 2017 is a month-long program (July 3-28, 2017) where participants gain hands-on experience with TensorFlow through individual-based projects with the goal of implementing new deep learning related ideas, and/or already-published ideas. We are looking for approximately 20 participants. More than a dozen industry experts with strong backgrounds in deep learning and TensorFlow implementation will serve as project mentors to guide participants.

FAQs

Q: What are we doing during the one month program?

A: Basically, we design a deep learning model and fully implement using TensorFlow. It is also possible to (re) implement a published paper (by others) and adapt it for new datasets. Based on this, each participants will propose their own plans in their application.

Q: What type of Visa is required for foreigners?

A: No visa is required for most countries. Please check at http://www.immigration.go.kr/HP/IMM80/imm_04/imm_p01/vm3.jsp

Q. Can I apply for partial participation? (i.e. only weekends)

A: Unfortunately No.

Q: Is this only for students?

A: No. Anyone who can spend one full month in Jeju Island in Korea, and work from 10AM-5PM during the weekdays can apply.

Q: Is this a contest?

A: No, this is not a contest. Individuals will have different projects.

Q: Is this a training or teaching program?

A: This is not specifically a teaching event. The applicant should have good understandings on programming, machine learning/deep learning, and TensorFlow. However, we will provide mentors to assist you on your project.

Q: Will data for training be provided or it is up to participants?

A: We will provide some public data sets but participants can also utilize their own data.

Q: Should I bring my laptop?

A: We won’t provide PCs. You need to bring your laptop. However, we will provide cloud server credits.

Q: Training takes a lot of time and computing power. Does the camp provide any support?

A: We will provide cloud server credits.

Q: What are the criteria for selecting applicants?

A: There is no formal criteria, but we are looking for interesting and feasible projects.

Q: What if the attendee cannot complete the work that they submitted?

A: There is no penalty, but mentors will guide each participant to success.

Q: Can developers/researchers working on longer term projects like http://ai-on.org/projects/cardiac-mri-segmentation.html apply? In this case, one month may not be sufficient time to finish the work. Is it OK to apply?

A: It’s OK. As long as the project is interesting, we will consider it. You can also propose a small portion of a larger project that you wish to work on for a month.

Q: Is this only for deep learning? Can I propose a reinforcement learning project?

A: Yes, reinforcement learning is acceptable. Feel free to include other types of interesting machine learning projects.

Q: Can I participate in a keynote session or open seminars even though I am not a camp participant?

A: Yes, the keynote and open seminars are open to everyone. We will have a separate announcement regarding keynote sessions and open seminars.

Q: Will you also be recruiting staff members for the camp?

A: Sorry, but we have no current plans to recruit staff members.

Q: I do have more questions. Where should I contact?

A: Please use the issue (https://github.com/TensorFlowKR/MLJejuCamp/issues) of this page to ask questions.

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

.

텐서플로  https://www.tensorflow.org/

구글(Google)사에서 개발한 기계 학습(machine learning) 엔진. 검색, 음성 인식, 번역 등의 구글 앱에 사용되는 기계 학습용 엔진으로, 2015년에 공개 소스 소프트웨어(open source software)로 전환되었다.

 텐서플로는 C++ 언어로 작성되었고, 파이선(Python) 응용 프로그래밍 인터페이스(API)를 제공한다.

텐서플로는 빠르고 유연하여 한 대의 스마트 폰에서도 운영될 수 있고, 데이터센터의 수천 대 컴퓨터에서도 동작될 수 있다.

Google : https://googleblog.blogspot.kr/2015/11/tensorflow-smarter-machine-learning-for.html

Github : https://github.com/tensorflow/tensorflow

FB Sight에 오신것을 환영합니다 (Terms of Service)!  : http://fbsight.com/c/TF-KR

TensorFlow™ is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. TensorFlow was originally developed by researchers and engineers working on the Google Brain Team within Google's Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well.

.

http://tensorflow.org/

TensorsFlowing : check out http://tensorflow.org