Welcome — Theano 0.7 documentation Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. Theano features: tight integration with NumPy – Use numpy.ndarray in Theano-compiled functions.transparent use of a GPU – Perform data-intensive computations much faster than on a CPU.efficient symbolic differentiation – Theano does your derivatives for functions with one or many inputs.speed and stability optimizations – Get the right answer for log(1+x) even when x is really tiny.dynamic C code generation – Evaluate expressions faster.extensive unit-testing and self-verification – Detect and diagnose many types of errors. Theano has been powering large-scale computationally intensive scientific investigations since 2007. But it is also approachable enough to be used in the classroom (University of Montreal’s deep learning/machine learning classes). 2017/11/15: Release of Theano 1.0.0. git clone How do I?
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:
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
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