Two Sigma: Using News to Predict Stock Movements
Use news analytics to predict stock price performance
1st place - $25,000
2nd place - $20,000
3rd place - $15,000
4th through 7th place - $10,000 each
This is a two-stage competition consisting of a Submission period and a Scoring period. In the Submission period, entrants will train their models in Kaggle Kernels. During the Scoring period, models submitted at the end of the submission period will be evaluated against regularly updated news and market data.
Start Date: 9/25/2018
Rules Acceptance/Team Merger Deadline: 1/2/2019
Submission Deadline: 1/8/2019 After this date, we will not be taking any more submissions. You can sit back and watch the leaderboard unfold. Remember to select your two best submissions to be rescored. In this competition we will not auto-select your two submissions.
End Date: 7/15/2019
https://www.kaggle.com/c/two-sigma-financial-news
Use news analytics to predict stock price performance
1st place - $25,000
2nd place - $20,000
3rd place - $15,000
4th through 7th place - $10,000 each
This is a two-stage competition consisting of a Submission period and a Scoring period. In the Submission period, entrants will train their models in Kaggle Kernels. During the Scoring period, models submitted at the end of the submission period will be evaluated against regularly updated news and market data.
Start Date: 9/25/2018
Rules Acceptance/Team Merger Deadline: 1/2/2019
Submission Deadline: 1/8/2019 After this date, we will not be taking any more submissions. You can sit back and watch the leaderboard unfold. Remember to select your two best submissions to be rescored. In this competition we will not auto-select your two submissions.
End Date: 7/15/2019
https://www.kaggle.com/c/two-sigma-financial-news
Kaggle
Two Sigma: Using News to Predict Stock Movements
Use news analytics to predict stock price performance
Brilliant post on #CS and #Software about strategy and psychology of Software Development, which is highly applicable to Data Science too.
“Imaginary Problems Are the Root of Bad Software”
https://medium.com/s/story/imaginary-problems-d4f2921bd1b8
“Imaginary Problems Are the Root of Bad Software”
https://medium.com/s/story/imaginary-problems-d4f2921bd1b8
Medium
Imaginary Problems Are the Root of Bad Software
Just because they're fun to solve doesn't mean they're relevant
Необычные материалы в робототехнике.
У каждого робота должен быть блестящий металлический зад, это знают все. Но в реальных роботах металла не так уж и много — с ним соседствуют пластики, композиты и силикон, а порой и совсем нестандартные субстанции: http://amp.gs/hCG8
У каждого робота должен быть блестящий металлический зад, это знают все. Но в реальных роботах металла не так уж и много — с ним соседствуют пластики, композиты и силикон, а порой и совсем нестандартные субстанции: http://amp.gs/hCG8
Comparison of top data science libraries for Python, R and Scala [Infographic]
https://medium.com/activewizards-machine-learning-company/comparison-of-top-data-science-libraries-for-python-r-and-scala-infographic-574069949267
https://medium.com/activewizards-machine-learning-company/comparison-of-top-data-science-libraries-for-python-r-and-scala-infographic-574069949267
Medium
Comparison of top data science libraries for Python, R and Scala [Infographic]
Data science is a promising and exciting field, developing rapidly. The area of data science use cases and influence is continuously…
How to train an algorithm to successfully pass the "Sonic The Hedgehog" game? Sergey Kolesnikov together with his team took the 4th place out of 900+ in the Open AI contest, and now is telling how to reach it. https://medium.com/swlh/at-the-speed-of-reinforcement-learning-an-openai-contest-story-6ed34fe7a3bb
Medium
At the Speed of Reinforcement Learning: an OpenAI Contest Story
Hello! I’m Sergey Kolesnikov, Senior Data Scientist at Dbrain, a lecturer at MIPT and HSE and open-source developer. Mostly, I am…
Most recent version of Andrew Ng’s book Machine Learning Yearning
Link: https://gallery.mailchimp.com/dc3a7ef4d750c0abfc19202a3/files/5dd91615-3b3f-4f5d-bbfb-4ebd8608d330/Ng_MLY01_13.pdf
Link: https://gallery.mailchimp.com/dc3a7ef4d750c0abfc19202a3/files/5dd91615-3b3f-4f5d-bbfb-4ebd8608d330/Ng_MLY01_13.pdf
Top AI Interview Questions & Answers — Acing the AI Interview
https://medium.com/acing-ai/top-ai-interview-questions-answers-acing-the-ai-interview-61bf52ca34d4
https://medium.com/acing-ai/top-ai-interview-questions-answers-acing-the-ai-interview-61bf52ca34d4
Medium
Top Data Science Interview Questions & Answers
Answers to AI and Data Science interview questions asked repeatedly.
SOTAWHAT - A script to keep track of state-of-the-art AI research
https://huyenchip.com/2018/10/04/sotawhat.html
https://github.com/chiphuyen/sotawhat.
https://huyenchip.com/2018/10/04/sotawhat.html
https://github.com/chiphuyen/sotawhat.
Chip Huyen
SOTAWHAT - A script to keep track of state-of-the-art AI research
While doing research, I often find myself wondering: “What’s the state-of-the-art result for XYZ right now?” I just want a tool that returns the summary of the latest SOTA research. My usual go-to place is Google, but I quickly realize that Google often returns:
"Mathematics for Machine Learning": drafts for all chapters now available
https://mml-book.github.io/ https://www.reddit.com/r/MachineLearning/comments/9lzabc/p_mathematics_for_machine_learning_drafts_for_all/
https://mml-book.github.io/ https://www.reddit.com/r/MachineLearning/comments/9lzabc/p_mathematics_for_machine_learning_drafts_for_all/
Reddit
From the MachineLearning community on Reddit
Explore this post and more from the MachineLearning community
mmdetection
mmdetection is an open source object detection toolbox based on PyTorch. It is a part of the open-mmlab project developed by Multimedia Laboratory, CUHK.
Major features
- Modular Design
One can easily construct a customized object detection framework by combining different components.
- Support of multiple frameworks out of box
The toolbox directly supports popular detection frameworks, e.g. Faster RCNN, Mask RCNN, RetinaNet, etc.
- Efficient
All basic bbox and mask operations run on GPUs now. The training speed is about 5% ~ 20% faster than Detectron for different models.
- State of the art
This was the codebase of the MMDet team, who won the COCO Detection 2018 challenge.
https://github.com/open-mmlab/mmdetection
mmdetection is an open source object detection toolbox based on PyTorch. It is a part of the open-mmlab project developed by Multimedia Laboratory, CUHK.
Major features
- Modular Design
One can easily construct a customized object detection framework by combining different components.
- Support of multiple frameworks out of box
The toolbox directly supports popular detection frameworks, e.g. Faster RCNN, Mask RCNN, RetinaNet, etc.
- Efficient
All basic bbox and mask operations run on GPUs now. The training speed is about 5% ~ 20% faster than Detectron for different models.
- State of the art
This was the codebase of the MMDet team, who won the COCO Detection 2018 challenge.
https://github.com/open-mmlab/mmdetection
GitHub
GitHub - open-mmlab/mmdetection: OpenMMLab Detection Toolbox and Benchmark
OpenMMLab Detection Toolbox and Benchmark. Contribute to open-mmlab/mmdetection development by creating an account on GitHub.