Lasagne: light weighted Theano extension, Theano can be used explicitly
Keras: is a minimalist, highly modular neural network library in the spirit of Torch, written in Python, that uses Theano under the hood for fast tensor manipulation on GPU and CPU. It was developed with a focus on enabling fast experimentation.
Pylean2: wrapper for Theano, yaml, experimental oriented.
Caffe: CNN oriented deep learning framework using c++, with python wrapper, easy model definitions using prototxt.
Theano: general gpu math
nolearn: a probably even simpler one
you can find more here.
For Lasagne and nolearn, they are still in the rapid develop stage, so they changes a lot. Be careful with the versions installed, they need to match each other. If you are having problems such as “cost must be a scalar”, you can refer link here to solve it by uninstall and reinstall them.
pip uninstall Lasagne pip uninstall nolearn pip install -r https://raw.githubusercontent.com/dnouri/kfkd-tutorial/master/requirements.txt
$ brew install mercurial
If you see errors like:
clang: error: unknown argument: '-mno-fused-madd' [-Wunused-command-line-argument-hard-error-in-future] clang: note: this will be a hard error (cannot be downgraded to a warning) in the future
you can disable the ‘warning’ (which is now showing as error ) by:
$ ARCHFLAGS=-Wno-error=unused-command-line-argument-hard-error-in-future \ brew install mercurial
Again, after the install successed, if you see linking error:Error: Could not symlink file: /usr/local/Cellar/mercurial/2.9/share/man/man5/hgrc.5
/usr/local/share/man/man5 is not writable. You should change its permissions.
You can change the permission. It is said to be safe to change the permission for the whole /usr/local. If you don’t want to do so, just do it for this case
$ sudo chown -R 'your-user-name' /usr/local/share/man/man5 $ brew link mercurial
Book + Codes <http://gnosis.cx/TPiP/>
Other good books
Code Like a Pythonista: Idiomatic Python
Thinkpython (wow, this is the newest version December 2012)
Anyone want to learn/improve should definitely look at places,where you will find your own recipe:
Always a fan of Python, anyway, it’s just so good.
A whole list of free books <original url>
- Think Stats
- Dive Into Python
- A Byte Of Python
- Think Complexity
- Dive Into Python 3
- DJANGO TUTORIAL
- Building Skills In OOP
- Pyramid For Humans
- Flask Microframework
- Building Skills In Python
- Kivy Programming Guide
- Snake Wrangling For Kids
- An Introduction To Python
- Programmez Avec Python 2
- Programmez Avec Python 3
- Python Module Of The Week
- Learn Python The Hard Way
- The Standard Python Library
- Building Skills In Programming
- Python Scientific Lecture Notes
- Making Games With Python & Pygame
- Python 101 (an introduction to python)
- How To Think Like A Computer Scientist
- Natural Language Processing With Python
- Programming Computer Vision With Python
really really Awesome post. — A paw detection using Python ndimage.
milk is the machine learning package written in python. It also comes with a complimentary data set called milksets which includes several U.C.I machine learning dataset.
from milksets import wine
features,labels = wine.load()
features will be a 2d-numpy.ndarray of features (noSample * noFeatureDim) and labels will be a 1d-numpy.ndarray of labels starting at 0 through N-1 (independently of how the labels were coded in the original data).
Below is an example using milk -random forest to predict the labels for the wine data. Three classes, feature is a (178L, 13L) np-matrix. Sample with maker ‘0’ is the correct predictions, with maker ‘x’ is the incorrect prediction. It takes some time to do the prediction, the cross-validation accuracy = 0.943820224719.
I wanted to be able to select and crop some region of the figures in python interactively. Here’s some ways that I found quite useful. You could modify the code and adapt to your need.
And superisingly, matplot has the same function of ginput just as in matlab.
It seems that python-graph is a nice tool. To install it, I used
the easy install.
My desktop is Win7 with Git, so install easy_install using:
$ curl -O http://python-distribute.org/distribute_setup.py
$ python distribute_setup.py
I found that my Git actually can not local where the easy_install is. So
simply way: find by your self. You may find a application file “easy_install.exe”
within your python folder. Then use:
Then, you done 🙂