SymPy
Caffe | Deep Learning Framework
Natural Language Toolkit — NLTK 3.2.4 documentation
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:
Matplotlib: Python plotting — Matplotlib 2.0.2 documentation
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
5. Data Structures
This chapter describes some things you’ve learned about already in more detail, and adds some new things as well. 5.1. More on Lists The list data type has some more methods. list.append(x) Add an item to the end of the list. list.extend(iterable) Extend the list by appending all the items from the iterable. list.insert(i, x) Insert an item at a given position. list.remove(x) Remove the first item from the list whose value is equal to x. list.pop([i]) Remove the item at the given position in the list, and return it. list.clear() Remove all items from the list. list.index(x[, start[, end]]) Return zero-based index in the list of the first item whose value is equal to x. The optional arguments start and end are interpreted as in the slice notation and are used to limit the search to a particular subsequence of the list. list.count(x) Return the number of times x appears in the list. list.sort(*, key=None, reverse=False) list.reverse() Reverse the elements of the list in place. list.copy() 5.1.1. 5.2.
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random — Generate pseudo-random numbers — Python v3.0.1 documentation
This module implements pseudo-random number generators for various distributions. For integers, uniform selection from a range. For sequences, uniform selection of a random element, a function to generate a random permutation of a list in-place, and a function for random sampling without replacement. On the real line, there are functions to compute uniform, normal (Gaussian), lognormal, negative exponential, gamma, and beta distributions. For generating distributions of angles, the von Mises distribution is available. Almost all module functions depend on the basic function random(), which generates a random float uniformly in the semi-open range [0.0, 1.0). The functions supplied by this module are actually bound methods of a hidden instance of the random.Random class. Class Random can also be subclassed if you want to use a different basic generator of your own devising: in that case, override the random(), seed(), getstate(), and setstate(). Bookkeeping functions: random.seed([x]) M.
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Deeplearning4j: Open-source, distributed deep learning for the JVM
Torch | Scientific computing for LuaJIT.
