Welcome — Pylearn2 dev documentation Warning This project does not have any current developer. We will continue to review pull requests and merge them when appropriate, but do not expect new development unless someone decides to work on it. There are other machine learning frameworks built on top of Theano that could interest you, such as: Blocks, Keras and Lasagne. Don’t expect a clean road without bumps! Pylearn2 is a machine learning library. Researchers add features as they need them. There is no PyPI download yet, so Pylearn2 cannot be installed using e.g. pip. git clone To make Pylearn2 available in your Python installation, run the following command in the top-level pylearn2 directory (which should have been created by the previous command): You may need to use sudo to invoke this command with administrator privileges. python setup.py develop --user This command will also compile the Cython extensions required for e.g. pylearn2.train_extensions.window_flip. Data path Ian J.
PyCuda/Examples/2DFFT - Andreas Klöckner's wiki This code does the fast Fourier transform on 2d data of any size. It used the transpose split method to achieve larger sizes and to use multiprocessing. The no of parts the input image is to be split, is decided by the user based on the available GPU memory and CPU processing cores. jackin@opt.utsunomiya-u.ac.jp Toggle line numbers 1 import numpy 2 import scipy.misc 3 import numpy.fft as nfft 4 import multiprocessing 5 6 from pyfft.cuda import Plan 7 from pycuda.tools import make_default_context 8 import pycuda.tools as pytools 9 import pycuda.gpuarray as garray 10 import pycuda.driver as drv 11 12 13 class GPUMulti(multiprocessing.Process): 14 def __init__(self, number, input_cpu, output_cpu): 15 multiprocessing.Process. CategoryPyCuda CategoryPyCuda CategoryPyCuda
Caffe | Deep Learning Framework numexpr - Fast numerical array expression evaluator for Python and NumPy. Please be aware that the numexpr project has been migrated to GitHub. This site has been declared unmaintained as of 2014-01-21. Sorry for the inconveniences. -- Francesc Alted What It Is The numexpr package evaluates multiple-operator array expressions many times faster than NumPy can. Also,numexpr implements support for multi-threading computations straight into its internal virtual machine, written in C. It is also interesting to note that, as of version 2.0, numexpr uses the new iterator introduced in NumPy 1.6 so as to achieve better performance in a broader range of data arrangements. Finally, numexpr has support for the Intel VML (Vector Math Library) -- integrated in Intel MKL (Math Kernel Library) --, allowing nice speed-ups when computing transcendental functions (like trigonometrical, exponentials...) on top of Intel-compatible platforms. Examples of Use Using it is simple: >>> import numpy as np>>> import numexpr as ne >>> a = np.arange(1e6)>>> b = np.arange(1e6) and fast...
Rands In Repose Popular Deep Learning Tools – a review Deep Learning is the hottest trend now in AI and Machine Learning. We review the popular software for Deep Learning, including Caffe, Cuda-convnet, Deeplearning4j, Pylearn2, Theano, and Torch. Deep Learning is now of the hottest trends in Artificial Intelligence and Machine Learning, with daily reports of amazing new achievements, like doing better than humans on IQ test. In 2015 KDnuggets Software Poll, a new category for Deep Learning Tools was added, with most popular tools in that poll listed below. Pylearn2 (55 users)Theano (50)Caffe (29)Torch (27)Cuda-convnet (17)Deeplearning4j (12)Other Deep Learning Tools (106) I haven’t used all of them, so this is a brief summary of these popular tools based on their homepages and tutorials. Theano & Pylearn2: Theano and Pylearn2 are both developed at University of Montreal with most developers in the LISA group led by Yoshua Bengio. Caffe: Torch & OverFeat: Torch is written in Lua, and used at NYU, Facebook AI lab and Google DeepMind. Cuda: Related:
Sage - French » Fully Distributed Teams: are they viable? It has become increasingly common for technology companies to run as Fully Distributed teams. That is, teams that collaborate primarily over the web rather than using informal, face-to-face communication as the main means of collaborating. This has only become viable recently due to a mix of factors, including: the rise of “cloud” collaboration services (aka “web 2.0″ software) as exemplified by Google Apps, Dropbox, and SalesForcethe wide availability of high-speed broadband in homes that rivals office Internet connections (e.g. home cable and fiber)real-time text, audio and video communication platforms such as IRC, Google Talk, and Skype Thanks to these factors, we can now run Fully Distributed teams without a loss in general productivity for many (though not all) roles. In my mind, there are three models for scaling number of employees in a growing company in the wild today. Vertically Scaled: Fully co-located team in a single office. Related Reading Discuss on Hacker News
Deep Learning Software | NVIDIA Developer Powerful Tools for Data Scientists NVIDIA’s Deep Learning GPU Training System puts the power of deep learning in the hands of data scientists and researchers. Quickly design the best deep neural network (DNN) for your data using real-time network behavior visualization. Best of all, DIGITS is a complete, interactive system, so you don’t have to write any code to train neural networks. NVIDIA DIGITS Monitoring Neural Network Training In-Progress GPU-Accelerated Tools and Libraries cuDNN The NVIDIA CUDA® Deep Neural Network library accelerates widely used open-source deep learning frameworks such as Caffe, Theano, Tensorflow, and Torch. cuBLAS The NVIDIA CUDA Basic Linear Algebra Subroutines library is a GPU-accelerated version of the complete standard BLAS library that delivers 6x to 17x faster performance than the latest MKL BLAS, providing GPU acceleration for BLAS routines widely used in deep learning. cuSPARSE CUDA Toolkit
SymPy 7 Ways to Leverage Your Time to Increase Your Productivity We’re all busy people. Some people, though, are busier than we’d ever imagine, yet are somehow are able to stay on top of things so well they seem to go about their life in a lackadaisical manner, while we struggle to produce good work and maintain a household. What’s their secret? Why do they seem to have everything figured out; always unstressed and ready to go? Leverage. Sure, tactics like maintaining “to-do” lists (or “done” lists), setting goals, and decreasing the amount and time of meetings can all help. Leverage is an awesome force–it allows us to multiply our abilities by applying a little pressure to something. In life, we can leverage our time, and here are seven ways to do just that: Get it out of your head. Leverage is only useful to us if we’re using it in the right direction: if we let the pressures of our lives get to us so much that we feel like we’re drowning, leverage is to blame. Or a better tomorrow! What about you?
Matlab Community Detection Toolbox download Easy threading with Futures To run a function in a separate thread, simply put it in a Future: >>> A=Future(longRunningFunction, arg1, arg2 ...) It will continue on its merry way until you need the result of your function. You can read the result by calling the Future like a function, for example: >>> print A() If the Future has completed executing, the call returns immediately. A few caveats: Since one wouldn't expect to be able to change the result of a function, Futures are not meant to be mutable. The Future only runs the function once, no matter how many times you read it. For more information on Futures, and other useful parallel programming constructs, read Gregory V.