Must Watch Data Science Videos from SciPy Conference 2015
Introduction This was in my first year of engineering degree. A hungry, home-food sick student (me) was treated (by a college senior) with a lavish buffet in one of the best five star hotels in Mumbai! You get served with so many dishes that you struggle to decide where to start, what to taste and what to eat! Why is this relevant here? Well, I had a similar feeling when I looked at the videos fro
scikit-learn: machine learning in Python
Simple and efficient tools for predictive data analysis Accessible to everybody, and reusable in various contexts Built on NumPy, SciPy, and matplotlib Open source, commercially usable - BSD license Classification Identifying which category an object belongs to. Applications: Spam detection, image recognition. Algorithms: Gradient boosting, nearest neighbors, random forest, logistic regression, an
1.6. Scipy : high-level scientific computing — Scipy lecture notes
1.6. Scipy : high-level scientific computing¶ Authors: Gaël Varoquaux, Adrien Chauve, Andre Espaze, Emmanuelle Gouillart, Ralf Gommers Scipy The scipy package contains various toolboxes dedicated to common issues in scientific computing. Its different submodules correspond to different applications, such as interpolation, integration, optimization, image processing, statistics, special functions,
1
リリース、障害情報などのサービスのお知らせ
最新の人気エントリーの配信
処理を実行中です
j次のブックマーク
k前のブックマーク
lあとで読む
eコメント一覧を開く
oページを開く
Feature request: dynamic time warping (dtw) function · Issue #7638 · scipy/scipy
https://conference.scipy.org/proceedings/scipy2010/pdfs/seabold.pdf
https://conference.scipy.org/proceedings/scipy2015/pdfs/matthew_rocklin.pdf
Keynote: Data Science at the New York Times | SciPy 2015 | Chris Wiggins
QQPOKERONLINE - Situs Judi QQ Poker dan IDN Slot Online Terpercaya
