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Deep Learning libraries and first experiments with Theano

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In recent years, neural networks and deep learning techniques have shown to perform well on many
problems in image recognition, speech recognition, natural language processing and many other tasks.
As a result, a large number of libraries, toolkits and frameworks came out in different languages and
with different purposes. In this report, firstly we take a look at these projects and secondly we choose the
framework that best suits our needs: Theano. Eventually, we implement a simple convolutional neural net
using this framework to test both its ease-of-use and efficiency.

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Deep Learning libraries and first experiments with Theano

  1. 1. Deep Learning libraries and first experiments with Theano • March 30, 2015 Deep Learning libraries and first experiments with Theano Vincenzo Lomonaco Alma Mater Studiorum - University of Bologna vincenzo.lomonaco@studio.unibo.it Abstract In recent years, neural networks and deep learning techniques have shown to perform well on many problems in image recognition, speech recognition, natural language processing and many other tasks. As a result, a large number of libraries, toolkits and frameworks came out in different languages and with different purposes. In this report, firstly we take a look at these projects and secondly we choose the framework that best suits our needs: Theano. Eventually, we implement a simple convolutional neural net using this framework to test both its ease-of-use and efficiency. 1. Introduction D eep learning (deep structured learning or hierarchical learning) is a set of al- gorithms in machine learning that at- tempt to model high-level abstractions in data by using model architectures composed of mul- tiple non-linear transformations. In the last decade, the real impact of deep learning on the industry and a renewed interest in the re- search in this area led it to become part of many state-of-the-art systems in different dis- ciplines, particularly that of computer vision and automatic speech recognition. Advances in hardware have also been an important enabling factor for the resurgence of neural networks and deep learning architectures. In particular, powerful graphics processing units (GPUs) are highly suited for the kind of number crunch- ing, matrix/vector math involved in machine learning. GPUs have been shown to speed up training algorithms by orders of magnitude, bringing running times of weeks back to days. However, It is well known that machine learn- ing comes with huge maintenance costs and it is extremely hard to debug, in particular when the algorithms are greatly optimized for spe- cific hardwares like GPUs. For this reason, a large number of libraries and software utilities came out to help developers to focus on the model side letting the framework or the library do the rest. In the following section we take a look at different open source projects that fol- low this path. Then we focus on the four most popular general-purpose framework, explain- ing them in greater detail and choosing that one the best suits our needs and our resources. In the second part of the report, we test the framework developing a simple convolutional neural net and performing same experiments with a known dataset. Eventually, in the last section we draw the main conclusions. 2. Available Deep Learning libraries and frameworks In this section we provide an overview about all the main projects provided by the deep learning community that can have different abstraction levels and different purposes. In fact, each of them could be particular help- ful for a specific task. In general, they came from research groups both from the academy and the industry and are open-source, licensed with BSD or similar. Theano [24] is a Python library that al- lows you to define, optimize, and evalu- ate mathematical expressions involving multi- dimensional arrays efficiently. 1
  2. 2. Deep Learning libraries and first experiments with Theano • March 30, 2015 Pylearn2 [26] is a machine learning library. Most of its functionality is built on top of Theano. This means you can write Pylearn2 plugins (new models, algorithms, etc) using mathematical expressions, and Theano will op- timize and stabilize those expressions for you, and compile them to a back-end of your choice (CPU or GPU). Torch [25] is a scientific computing frame- work with wide support for machine learn- ing algorithms. It is easy to use and efficient, thanks to an easy and fast scripting language, LuaJIT, and an underlying C/CUDA imple- mentation. Deeplearning4j [5] is the first commercial- grade, open-source, distributed deep-learning library written for Java and Scala. Integrated with Hadoop and Spark, DL4J is designed to be used in business environments, rather than as a research tool. It aims to be cutting-edge plug and play, more convention than configu- ration, which allows for fast prototyping for non-researchers. Caffe [29] is a deep learning framework made with expression, speed, and modularity in mind. It is developed by the Berkeley Vi- sion and Learning Center (BVLC) and by com- munity contributors. Yangqing Jia created the project during his PhD at UC Berkeley. Caffe is released under the BSD 2-Clause license. NVIDIA cuDNN [19] is a GPU-accelerated library of primitives for deep neural networks. It emphasizes performance, ease-of-use, and low memory overhead. NVIDIA cuDNN is designed to be integrated into higher-level ma- chine learning frameworks, such as UC Berke- ley’s popular Caffe software. The simple, drop- in design allows developers to focus on de- signing and implementing neural net models rather than tuning for performance, while still achieving the high performance modern paral- lel computing hardware affords. DeepLearnToolbox [6] is a Matlab/Octave toolbox for deep learning. Includes deep be- lief bets, stacked autoencoders, convolutional neural nets, convolutional autoencoders and vanilla neural net. It is in its early stages of development. Cuda-Convnet2 [3] is a fast C++/CUDA im- plementation of convolutional (or more gen- erally, feed-forward) neural networks. It can model arbitrary layer connectivity and net- work depth. Any directed acyclic graph of layers will do. Training is done using the back- propagation algorithm and now it supports multi-GPUs training parallelism. RNNLM [30] is a toolkit led by Tomas Mikolov that can be used to train, evaluate and use neural network based language models. RNNLIB [27] is a recurrent neural network library for sequence learning problems. Ap- plicable to most types of spatio-temporal data, it has proven particularly effective for speech and handwriting recognition. LUSH [12] is an object-oriented program- ming language designed for researchers, ex- perimenters, and engineers interested in large- scale numerical and graphic applications. Lush is designed to be used in situations where one would want to combine the flexibility of a high-level, weakly-typed interpreted language, with the efficiency of a strongly-typed, natively- compiled language, and with the easy integra- tion of code written in C, C++, or other lan- guages. Eblearn.lsh [9] is a LUSH-based machine learning library for doing energy-based learn- ing led by Koray Kavukcuoglu. It includes code for “predictive sparse decomposition” and other sparse auto-encoder methods for un- supervised learning. Eblearn [34] is a C++ machine learning library with a BSD license for energy- 2
  3. 3. Deep Learning libraries and first experiments with Theano • March 30, 2015 based learning, convolutional networks, vi- sion/recognition applications, etc. EBLearn is primarily maintained by Pierre Sermanet at NYU. MShadow [15] is a lightweight CPU/GPU Matrix/Tensor Template Library in C++/CUDA. The goal of mshadow is to sup- port efficient, device invariant and simple tensor library for machine learning project that aims for both simplicity and performance. Sup- ports CPU/GPU/Multi-GPU and distributed system. Nengo [23] is a graphical and scripting based software package for simulating large-scale neural systems. To use Nengo, you define groups of neurons in terms of what they rep- resent, and then form connections between neural groups in terms of what computa- tion should be performed on those representa- tions. Nengo then uses the Neural Engineering Framework (NEF) to solve for the appropri- ate synaptic connection weights to achieve this desired computation. Nengo also supports var- ious kinds of learning. Nengo helps make de- tailed spiking neuron models that implement complex high-level cognitive algorithms. cudamat [31] is a Python module for per- forming basic dense linear algebra computa- tions on the GPU using CUDA. The current fea- ture set of cudamat is biased towards features needed for implementing some common ma- chine learning algorithms. Some feedforward neural networks and restricted Boltzmann ma- chines implementations are provided as exam- ples that come with cudamat. Gnumpy [35] is a Python module that inter- faces in a way almost identical to numpy, but does its computations on the GPU. It runs on top of cudamat. CUV Library [4] is a C++ framework with python bindings for easy use of Nvidia CUDA functions on matrices. It contains an RBM im- plementation, as well as annealed importance sampling code and code to calculate the parti- tion function exactly. It is provided by the AIS lab at University of Bonn. ConvNet [1] is a Matlab toolbox for convo- lutional neural networks, including invariang backpropagation algorithm (IBP). It has ver- sions for GPU and CPU, written on CUDA, C++ and Matlab. All versions work identi- cally. The GPU version uses kernels from Alex Krizhevsky’s library cuda-convnet2. neuralnetworks [17] is a Java implementa- tion of some of the algorithms for training deep neural networks. GPU support is provided via the OpenCL and Aparapi. The architec- ture is designed with modularity, extensibil- ity and pluggability in mind. At the moment it supports multilayer perceptron, restricted boltzmann machine, autoencoder, deep belief network, stacked autoencodeer, convolutional networks with max pooling, average pooling and stochastic pooling. convnetjs [2] is a Javascript library for train- ing Deep Learning models (mainly Neural Net- works) entirely in the browser. The software has no other dependencies and doesn’t require any installation. It currently supports com- mon neural network modules, classification (SVM/Softmax) and regression (L2) cost func- tions, a MagicNet class for fully automatic neu- ral network learning (automatic hyperparam- eter search and cross-validatations), ability to specify and train convolutional networks that process images, an experimental reinforcement learning module, based on Deep Q Learning. NuPIC [18] is a set of learning algorithms written in Python and C++ that implements an Hierarchal Temporal Memory, or HTM, first described in a white paper published by Nu- menta in 2009 [28]. The learning algorithms tries faithfully to capture how layers of neurons in the neocortex learn. 3
  4. 4. Deep Learning libraries and first experiments with Theano • March 30, 2015 PyBrain [33] is a modular Machine Learning Library for Python. Its goal is to offer flexi- ble, easy-to-use yet still powerful algorithms for Machine Learning Tasks and a variety of predefined environments to test and compare algorithms. It currently supports various algo- rithms for neural networks, for reinforcement learning (and the combination of the two), for unsupervised learning, and evolution. deepnet [7] is a GPU-based python imple- mentation of feed-forward neural nets, re- stricted boltzmann machines, deep belief nets, autoencoders, deep boltzmann machines, con- volutional nets and it is built on top of the cud- amat library by Vlad Mnih and cuda-convnet2 library by Alex Krizhevsky. DeepPy [8] tries to combine state-of-the-art deep learning models with a Pythonic interface in an extensible framework. It can run both on CPU or Nvidia GPUs when available (thanks to CUDArray). hebel [11] is a library for deep learning with neural networks in Python using GPU acceler- ation with CUDA through PyCUDA. It imple- ments the most important types of neural net- work models and offers a variety of different activation functions and training methods such as momentum, Nesterov momentum, dropout, and early stopping. Mocha [14] is a Deep Learning framework for Julia, inspired by the C++ framework Caffe. Efficient implementations of general stochastic gradient solvers and common layers in Mocha could be used to train deep/shallow (con- volutional) neural networks, with (optional) unsupervised pre-training via (stacked) auto- encoders. OpenDL [20] is a deep learning training li- brary based on Spark framework. The algo- rithms in OpenDL should be gradient update support like logistic regression (Softmax), back- propagation, autoEncoder, RBM, Convolution and so on, all of them can be incorporated into OpenDL framework. MGL [13] is a Common Lisp machine learn- ing library by Gàbor Melis with some parts originally contributed by Ravenpack Interna- tional. It mainly concentrates on various forms of neural networks (boltzmann machines, feed- forward and recurrent backprop nets). Most of MGL is built on top of MGL-MAT so it has BLAS and CUDA support. In general, the fo- cus is on power and performance not on ease of use. Gensim [10] is an open-source vector space modeling and topic modeling toolkit, imple- mented in the Python programming language, using NumPy, SciPy and optionally Cython for performance. It is specifically intended for han- dling large text collections, using efficient on- line algorithms. Gensim includes implementa- tions of tf–idf, random projections, deep learn- ing with Google’s word2vec algorithm (reim- plemented and optimized in Cython), hierar- chical Dirichlet processes (HDP), latent seman- tic analysis (LSA) and latent Dirichlet alloca- tion (LDA), including distributed parallel ver- sions. ND4J [16] is a scientific computing library for the JVM. It is meant to be used in production environments rather than as a research tool, which means routines are designed to run fast with minimum RAM requirements. its main features are: versatile n-dimensional array object, multiplatform functionality including GPUs, linear algebra and signal processing functions. As we saw, there are a great number of projects dealing with dense numeric computations both on CPUs and GPUs. Most of them are opti- mized to suit DL algorithms and offer a num- ber of primitives or classes to help developers to write easy and readable code. However they work at different abstraction levels and it is not uncommon to find projects that are based on other lower level projects. 4
  5. 5. Deep Learning libraries and first experiments with Theano • March 30, 2015 3. Most popular frameworks In this section we explore in greater details the most popular and general-purposes frame- works used today to solve various DL tasks. We decided to focus on 4 main projects: • Theano • Torch • Deeplearning4j • Caffe 3.1. Theano As we saide before, Theano is a Python library that lets you to define, optimize, and evaluate mathematical expressions, especially ones with multi-dimensional arrays (numpy.ndarray). Us- ing Theano it is possible to attain speeds rival- ing hand-crafted C implementations for prob- lems involving large amounts of data. It can also surpass C on a CPU by many orders of magnitude by taking advantage of recent GPUs. Theano combines aspects of a computer alge- bra system (CAS) with aspects of an optimizing compiler. It can also generate customized C code for many mathematical operations. This combination of CAS with optimizing compi- lation is particularly useful for tasks in which complicated mathematical expressions are eval- uated repeatedly and evaluation speed is crit- ical. For situations where many different ex- pressions are each evaluated once Theano can minimize the amount of compilation/analysis overhead, but still provide symbolic features such as automatic differentiation. In Theano there are two ways currently to use a GPUs, one of which only supports NVIDIA cards (CUDA backend) and the other, in de- velopment, that should support any OpenCL device as well as NVIDIA cards (GpuArray Backend). One thing to keep in mind is that the “build- ing blocks” you get in Theano are not ready- made neural network layer classes, but rather symbolic function expressions that are possible to compose into other expressions. The work is made at a slightly lower level of abstrac- tion, but this means there is lot more flexibility. (That said, if one needs ’plug and play’ neural networks, he can can use pylearn2 which is built on top of Theano). Theano was written at the LISA lab to sup- port rapid development of efficient machine learning algorithm and released under a BSD license. 3.2. Torch The goal of Torch is to have maximum flexi- bility and speed in building your scientific al- gorithms while making the process extremely simple. Torch comes with a large ecosystem of community-driven packages in machine learn- ing, computer vision, signal processing, par- allel processing, image, video, audio and net- working among others, and builds on top of the Lua community. At the heart of Torch are the popular neural network and optimization libraries which are simple to use, while having maximum flexibil- ity in implementing complex neural network topologies. You can build arbitrary graphs of neural networks, and parallelize them over CPUs and GPUs in an efficient manner. Torch core features are: • A powerful N-dimensional array. • Lots of routines for indexing, slicing, transposing, etc. • Amazing interface to C, via LuaJIT. • Linear algebra routines. • Neural network, and energy-based mod- els. • Numeric optimization routines. • Fast and efficient GPU support. • Embeddable, with ports to iOS, Android and FPGA backends. In the words of Soumith Chintala, one of the Author of Torch: It’s like building some kind of electronic contraption or, like, a Lego set. You just can plug in and plug out all these blocks that have different dynamics and that have complex algorithms within them. At the same time Torch is actually not extremely difficult to learn unlike, say, 5
  6. 6. Deep Learning libraries and first experiments with Theano • March 30, 2015 the Theano library. We’ve made it incredibly easy to use. We introduce someone to Torch, and they start churning out research really fast. So it has a slightly higher level of abstrac- tion then Theano, but It has nothing to do with the familiar and rich Python ecosystem. With respect to the GPU support, It works well with different backends depending on the task. CUDA is supported by installing the package cutorch. An alternative is to use NVidia CuDNN, which is very reliable, or cuda-convnet2 bindings, or nnbhwd package. 3.3. Deeplearning4j In a nutshell, Deeplearning4j lets you compose deep nets from various shallow nets, each of which form a layer. This flexibility lets you combine restricted Boltzmann machines, au- toencoders, convolutional nets and recurrent nets as needed in a distributed, production- grade framework on Spark, Hadoop and else- where. To sum up, the DL4J’s main features are: • A versatile n-dimensional array class. • GPU integration. • Scalable on Hadoop, Spark and Akka + AWS and other platforms. Torch, while powerful, was not designed to be widely accessible to the Python-based academic community, nor to corporate soft- ware engineers, whose lingua franca is Java. Deeplearning4j was written in Java to reflect the focus on industry and ease of use. In fact usability is often the limiting parameter that inhibits more widespread of deep-learning im- plementations. A great thing in DL4J is that you can choose CUDA compatible GPUs or native CPUs for your backend processing by changing just one line in a configuration file. Moreover, while both Torch7 and DL4J employ parallelism, DL4J’s parallelism is automatic. That is, the setting up of worker nodes and connections is automated, allowing users to bypass libs while creating a massively parallel network on Spark, Hadoop, or with Akka and AWS. 3.4. Caffe Caffe is developed by the Berkeley Vision and Learning Center (BVLC) and by community contributors released under the BSD 2-Clause license. Yangqing Jia created the project during his PhD at UC Berkeley. In one sip, Caffe is brewed for: • Expression: models and optimizations are defined as plaintext schemas instead of code. • Speed: for research and industry alike speed is crucial for state-of-the-art mod- els and massive data. • Modularity: new tasks and settings re- quire flexibility and extension. • Openness: scientific and applied progress call for common code, refer- ence models, and reproducibility. • Community: academic research, startup prototypes, and industrial applications all share strength by joint discussion and development in a BSD-2 project. Deep networks are compositional models that are naturally represented as a collection of inter-connected layers that work on chunks of data. Caffe defines a net layer-by-layer in its own model schema. The network defines the entire model bottom-to-top from input data to loss. As data and derivatives flow through the network in the forward and backward passes Caffe stores, communicates, and manipulates the information as blobs: the blob is the stan- dard array and unified memory interface for the framework. In terms of GPUs support it works both with CUDA and NVIDIA cuDNN. One lack at the moment is the multi-GPUs support. Currently, in fact, Caffe does work with multiple GPUs only in a standalone fashion. 6
  7. 7. Deep Learning libraries and first experiments with Theano • March 30, 2015 4. Our resources and the framework chosen In this section we provide a brief explanation regard the framework we chose. First of all, we limited our interest to the four framework detailed before. Then we considered that one, Theano, that best suits our needs. In fact we looked for a framework that has a good sup- port for Windows OS, has a good set of tuto- rials and examples, has great portability, is easy to use and is widely used in the aca- demic community. Torch7, the newest ver- sion of Torch, doesn’t seem to provide a com- plete Windows support. In fact, even if like Theano is optimized for a linux x64 machine, it doesn’t provide any documentation about the installation on Windows. Moreover, Caffe and Deeplearning4j are discarded from the start because caffe has only an unofficial Windows port and Deeplearning4j carries with him a lot of dependencies in the Java ecosystem that is not something we want to bear. Another reason to choose Theano instead of Torch7 is that is widely used in the academic community. Torch7 is used by Google DeepMind, the Face- book AI Research Group, the Computational Intelligence, Learning, Vision, and Robotics Lab at NYU and the Idiap Research Institute, but it is mainly because its main authors are part of these institutions. The main article of Theano, instead, has more than 300 citations from a large range of academics, including Y. Bengio, T. Mikolov, J. Goodfellow and a lot of research groups from many universities like the Bernstein Center for Computational Neuro- science at the Freie Universität Berlin, the Har- vard Intelligent Probabilistic Systems group, the Reservoir Lab at Ghent University in Ghent in Belgium, the Centre for Theoretical Neuro- science, University of Waterloo, the Center for Language and Speech Processing at the Johns Hopkins University and many others. Other great researchers in the area, instead, seem not to use either of them. Juergen Schmid- huber and his team at the Dalle Molle Institute of Artificial Intelligence is pushing for his own framework PyBrain; The Toronto crew led by Geoffrey Hinton, and the Stanford crew led by Andrew Ng, finally, seem to prefer custom implementations of ad-hoc algorithms rather than using general-purpose frameworks. 5. Experiments and results In this section we provide a brief use-case with Theano implementing a convolutional neural network and testing that on a subsampled NORB dataset. We used and modified the code provided in the Theano tutorial library[22] (which already im- plements a LeNet-5 for recognizing handwrit- ten digit images of the classic MNIST dataset) and then we performed two simple experi- ments on that, reaching a good level of accu- racy despite the low amount of training data. The dataset The original NORB dataset is in- tended for experiments in 3D object recogni- tion from shape. It contains images of 50 toys belonging to 5 generic categories: four-legged animals, human figures, airplanes, trucks, and cars. The objects are imaged by two cameras under 6 lighting conditions, 9 elevations (30 to 70 degrees every 5 degrees), and 18 azimuths (0 to 340 every 20 degrees). In this work we used a subsampled dataset in which each im- age is 32x32 pixels and we count 200 images for each category in the training set and 500 for the validation and the test set. The model The model is a slightly modifi- cation of that described in the original work by LeCun [21] about the experiments on the NORB dataset. In this case we consider one input channel of 32x32 pixels and we apply our first convolution layer using a 5x5 filter. We end up with 8 images 30x30 that are then sub- sampled with max-pooling to 8 images 15x15. After that, another convolution operation is ap- plied with a 6x6 filter and subsampled again to obtain 25 images 5x5. Before the full connected layer the last convolution is applied using a 5x5 filter. 7
  8. 8. Deep Learning libraries and first experiments with Theano • March 30, 2015 Figure 1: The LeNet-5 model used for the experiments. Taken from [32] the experiments After having modified the code to implement our model we decided to perform two simple experiments on our dataset to see how it works. Since stochastic gradient descent is used for the training, It was cho- sen to distinguish the experiments only for the maximum number of epoch: The first exper- iment up to 100, the second one up to 500. In the original code there was also an early- stopping parameter depending on the perfor- mance increment in each epoch but we chose to ignore it to see how the model works up to the overfitting. The other parameters were the batch size, set at 100, and the learning rate, set at 0.06 (note that these parameters stay the same during the whole training). Theano lets you decide on which hardware the code has to be executed without changing a single line of code. By default it searches for a GPU and if it is not found it does the job on the CPU. In our case the experiments were performed on a laptop with an Intel Core 2 Duo and a CPU usage-limit of 35%. The results In the table 1 it is possible to see the time used to train the model for each ex- periment and the error rate of the best model according to the validation set but computed on the joint validation and test set. Experiment time error rate Exp1-100ep 16.73m 22.36% Exp2-500ep 72.00m 22.16% Table 1: Eperiments results indicating training time and error rate of the best model found, computed on the whole test+validation set. In the first experiment the model could be trained in about five minutes using the full CPU, and the second one in about 25 minutes. The reached accuracy is good considering that only 200 images for each category are used in the training set. In figure 2 it is possible to see the mean of the negative log likelihood computed for each epoch during the training stage. In figure 3, instead, the error rate for both the validation set and the test set are plotted. Figure 2: The negative log likelihood computed in the training for each epoch of the first experiment. Figure 3: The error rate percentage for both validation and test set for each epoch in the first experi- ment. Note that if the performance on the validation set is not the best until that moment, the error rate on the test set is not computed. After some 8
  9. 9. Deep Learning libraries and first experiments with Theano • March 30, 2015 oscillations the error keeps decreasing until the the epoch 100 is reached. Figure 4 and figure 5 teach us what happens after the 100th epoch. The negative log likeli- hood keeps decreasing while the error rate on the validation set doesn’t decrease anymore, on the contrary it slightly increases. Figure 4: The negative log likelihood computed in the training for each epoch of the second experi- ment. Figure 5: The error rate percentage for both validation and test set for each epoch in the second exper- iment. 6. Conclusion In this report we took a look at different projects that came out in the last decade as a result for a renewed interest in deep learning and neural networks. They differ for objec- tives and features they offer, the programming language or the abstraction level used. We iso- lated 4 main projects that are found to be the most popular in the research community or in the industry context. All of them have a great flexibility and are easy-to-use (to some extent) even if they perform heavy computa- tional optimizations for both CPUs and GPUs. In the second part of the report we focused on Theano, that is the best choice with respect to our resources and needs. With it, we tried to implement a convolutional neural network using the code provided in the tutorial library. Then we applied this model on a subsampled NORB dataset and got the expected results. In conclusion, Theano seems to be very flexible and portable, bringing with him the beauty of the Python ecosystem, a good set of tutorials and a well documented code. References [1] Convnet. https://github.com/ sdemyanov/ConvNet. Accessed: 2015-03- 03. [2] convnetjs. http://cs.stanford.edu/ people/karpathy/convnetjs/. Accessed: 2015-03-03. [3] Cuda-convnet2. https://code.google. com/p/cuda-convnet2/. Accessed: 2015- 03-03. [4] Cuv library. http://www.ais.uni-bonn. de/deep_learning/downloads.html. Ac- cessed: 2015-03-03. [5] Deeplearning4j. http://deeplearning4j. org/. Accessed: 2015-03-03. [6] Deeplearntoolbox. https://github.com/ rasmusbergpalm/DeepLearnToolbox. Ac- cessed: 2015-03-03. [7] deepnet. https://github.com/ nitishsrivastava/deepnet. Accessed: 2015-03-03. 9
  10. 10. Deep Learning libraries and first experiments with Theano • March 30, 2015 [8] deeppy. https://github.com/ andersbll/deeppy. Accessed: 2015- 03-03. [9] Eblearn.lsh. http://koray.kavukcuoglu. org/code.html. Accessed: 2015-03-03. [10] Gensim. https://radimrehurek.com/ gensim/. Accessed: 2015-03-03. [11] hebel. https://github.com/ hannes-brt/hebel. Accessed: 2015- 03-03. [12] Lush. http://lush.sourceforge.net/. Accessed: 2015-03-03. [13] Mgl. http://melisgl.github.io/ mgl-pax-world/mgl-manual.html. Ac- cessed: 2015-03-03. [14] Mocha. https://github.com/pluskid/ Mocha.jl. Accessed: 2015-03-03. [15] Mshadow. https://github.com/tqchen/ mshadow. Accessed: 2015-03-03. [16] Nd4j. http://nd4j.org/. Accessed: 2015- 03-03. [17] neuralnetworks. https://github.com/ ivan-vasilev/neuralnetworks. Ac- cessed: 2015-03-03. [18] Nupic. http://numenta.org/. Accessed: 2015-03-03. [19] Nvidia cudnn. https://developer. nvidia.com/cuDNN. Accessed: 2015-03-03. [20] Opendl. https://github.com/ guoding83128/OpenDL. Accessed: 2015-03-03. [21] Norb: Generic object recognition in im- ages. http://www.cs.nyu.edu/~yann/ research/norb/, 2011. Accessed: 2015- 03-03. [22] Theano documentation: Deep learning tutorials. http://deeplearning.net/ tutorial/lenet.html, 2015. Accessed: 2015-03-03. [23] Trevor Bekolay, James Bergstra, Eric Hunsberger, Travis DeWolf, Terrence C Stewart, Daniel Rasmussen, Xuan Choo, Aaron Russell Voelker, and Chris Elia- smith. Nengo: a python tool for building large-scale functional brain models. Fron- tiers in neuroinformatics, 7, 2013. [24] James Bergstra, Olivier Breuleux, Frédéric Bastien, Pascal Lamblin, Razvan Pascanu, Guillaume Desjardins, Joseph Turian, David Warde-Farley, and Yoshua Bengio. Theano: a cpu and gpu math expression compiler. In Proceedings of the Python for scientific computing conference (SciPy), vol- ume 4, page 3. Austin, TX, 2010. [25] Ronan Collobert, Koray Kavukcuoglu, and Clément Farabet. Torch7: A matlab- like environment for machine learning. In BigLearn, NIPS Workshop, number EPFL- CONF-192376, 2011. [26] Ian J Goodfellow, David Warde-Farley, Pascal Lamblin, Vincent Dumoulin, Mehdi Mirza, Razvan Pascanu, James Bergstra, Frédéric Bastien, and Yoshua Bengio. Pylearn2: a machine learn- ing research library. arXiv preprint arXiv:1308.4214, 2013. [27] Alex Graves. Rnnlib: A recurrent neu- ral network library for sequence learning problems, 2008. [28] Jeff Hawkins and Dileep George. Hierar- chical temporal memory: Concepts, the- ory and terminology. Technical report, Technical report, Numenta, 2006. [29] Yangqing Jia, Evan Shelhamer, Jeff Don- ahue, Sergey Karayev, Jonathan Long, Ross Girshick, Sergio Guadarrama, and Trevor Darrell. Caffe: Convolutional archi- tecture for fast feature embedding. arXiv preprint arXiv:1408.5093, 2014. [30] Tomas Mikolov, Stefan Kombrink, Anoop Deoras, Lukar Burget, and Jan Cernocky. Rnnlm-recurrent neural network language modeling toolkit. In Proc. of the 2011 ASRU Workshop, pages 196–201, 2011. 10
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