scikit-learn
http://scikit-learn.org
0.15.2
>>> sklearn.__version__
https://pypi.python.org/pypi/scikit-learn/0.15.2
dependencies
scikit-learn is tested to work under Python 2.6, Python 2.7, and Python 3.4.
The required dependencies to build the software are
- NumPy >= 1.6.2
>>> numpy.__version__
https://pypi.python.org/pypi/numpy/1.10.1 (type: Python Wheel)http://www.numpy.org (Installing the SciPy Stack …) - SciPy >= 0.9
>>> scipy.__version__
http://www.scipy.org
ScyPy stack http://www.scipy.org/install.html
You can also build any of the SciPy packages from source, for instance if you want to get involved with development. This is easy for packages written entirely in Python, while others like NumPy require compiling C code.- Anaconda 392 Mb
Miniconda (4 Mb)
http://repo.continuum.io/miniconda/index.html - Enthought Canopy 300 Mb
- Python(x,y) (It is recommended to uninstall any other Python distribution before installing Python(x,y))
- WinPython 267 Mb
- Anaconda 392 Mb
- a working C/C++ compiler.
Create a separate environment
http://conda.pydata.org/docs/using/envs.html
—————————-
NumPy (4.6 MB) download
http://sourceforge.net/projects/numpy/files
the below notes are about building Numpy, which for most users is *not* the recommended way to install Numpy. Instead, use either a complete scientific Python distribution or a binary installer
——————————–
Dragomir Radev, September 2015
Here is how to install a specific older version of a Python library:
pip uninstall scikit-learn
or
pip uninstall sklearn
then
pip install scikit-learn==0.15.2
Hint: the following packages conflict with each other:
– scikit-learn ==0.15.2
– python 3.5*
http://conda.pydata.org
© Continuum Analytics
.tar.gz files
http://www.gzip.org
http://www.7-zip.org
Assignment on word similarity
https://class.coursera.org/nlpintro-001/forum/thread?thread_id=59 https://class.coursera.org/nlpintro-001/forum/thread?thread_id=59#post-244
DLNLP
http://web.eecs.umich.edu/~radev/dlnlp/list.txt
—————————————
CS224d: Deep Learning for Natural Language Processing
March-June 2015
http://cs224d.stanford.edu
—————————————
International Workshop on Semantic Evaluation 2015
http://alt.qcri.org/semeval2015
SDP 2015: Broad-Coverage Semantic Dependency Parsing
http://alt.qcri.org/semeval2015/task18
————————————–
NL generation & information extraction
https://class.coursera.org/nlpintro-001/forum/thread?thread_id=172#post-667
NL generation
https://class.coursera.org/nlpintro-001/forum/thread?thread_id=44#post-186
NACLO for Week 7
https://class.coursera.org/nlpintro-001/forum/thread?thread_id=27#post-566
week 6
https://class.coursera.org/nlpintro-001/forum/thread?thread_id=27#post-436
week 5
https://class.coursera.org/nlpintro-001/forum/thread?thread_id=27#post-435
week4
https://class.coursera.org/nlpintro-001/forum/thread?thread_id=27#post-361
week 3
https://class.coursera.org/nlpintro-001/forum/thread?thread_id=27#post-272
week 2
https://class.coursera.org/nlpintro-001/forum/thread?thread_id=27#post-182
week1
https://class.coursera.org/nlpintro-001/forum/thread?thread_id=27#post-99
Assignment 2, part 3A
https://class.coursera.org/nlpintro-001/forum/thread?thread_id=163#post-632
some good papers in NLP
https://class.coursera.org/nlpintro-001/forum/thread?thread_id=48#post-211
NLP libraries in Java
https://class.coursera.org/nlpintro-001/forum/thread?thread_id=33#post-128
this course is more introductory than …
https://class.coursera.org/nlpintro-001/forum/thread?thread_id=46#post-197
the assignments for this class have been developed and tested on Python 2.7 and NLTK 2.
volunteers: covering installation of Python and NLTK on different platforms