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Top 15 Deep Learning Software
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Top 15 Deep Learning Software

Top 15 Deep Learning Software
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Deep Learning Software : Deep Learning is a branch of machine learning for learning about multiple levels of representation and abstraction to make sense of the data such as images, sound, and text. It is a set of algorithms in machine learning which typically uses artificial neural networks to learn in multiple levels, corresponding to different levels of abstraction. The levels in these learned statistical models correspond to distinct levels of concepts, where higher level concepts are defined from lower level ones, and the same lower level concepts can help to define many higher level concepts. Deep learning architectures are Deep neural networks, Deep belief networks, Convolutional neural networks, Convolutional Deep Belief Networks, Deep Boltzmann Machines, Stacked Auto Encoders, Deep Stacking Networks, Tensor Deep Stacking Networks (T-DSN), Spike-and-Slab RBMs (ssRBMs), Compound Hierarchical-Deep Models, Deep Coding Networks and Deep Kernel Machines. Deep Learning applications are automatic speech recognition, image recognition and natural language processing.

Top Deep Learning Software :Neural Designer, Torch, Apache SINGA, Microsoft Cognitive Toolkit, Keras, Deeplearning4j, Theano, MXNet, H2O.ai, ConvNetJS, DeepLearningKit, Gensim, Caffe, ND4J and DeepLearnToolbox are some of the Top Deep Learning Software.

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 Top Deep Learning Software

Neural Designer, Torch, Apache SINGA, Microsoft Cognitive Toolkit, Keras, Deeplearning4j, Theano, MXNet, H2O.ai, ConvNetJS, DeepLearningKit, Gensim, Caffe, ND4J and DeepLearnToolbox are some of the Top Deep Learning Software.
1

Neural Designer

Neural Designer is a desktop application for data mining which uses neural networks, a main paradigm of machine learning. The software is developed by the startup company called Artelnics, based in Spain and founded by Roberto Lopez and Ismael Santana. Neural networks are mathematical models of the brain function, computational models which are inspired by central nervous systems, in particular the brain, which can be trained to perform certain tasks. Neural networks are capable of machine learning as well as pattern recognition. Neural networks are generally presented as systems of interconnected neurons, which can compute outputs from inputs. Neural network…

Neural Designer

Neural Viewer

2

Torch

Torch is an open source deep learning library and is a scientific computing framework with wide support for machine learning algorithms. It uses a fast scripting language LuaJIT, and a C implementation. It is easy to use and provides a very efficient implementation, using LuaJIT. Torch provides a powerful N-dimensional array, lots of routines for indexing, slicing, transposing, linear algebra routines, neural network, and energy-based models and numeric optimization routines . Torch is a scientific computing framework with wide support for machine learning algorithms that puts GPUs first. It offers an easy to use and efficient program to its users, thanks to an easy and fast scripting language, LuaJIT, and an underlying C/CUDA implementation. Users of Torch will be able to take advantage of its core features such as a powerful N-dimensional array, lots of routines for indexing, slicing, transposing, amazing interface to C, via LuaJIT, linear algebra routines, neural network, and energy-based models, numeric optimization routines, fast and efficient GPU support and embeddable, with ports to iOS, Android and FPGA…

Torch

3

Apache SINGA

Apache SINGA is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator. Incubation is required of all newly accepted projects until a further review indicates that the infrastructure, communications, and decision making process have stabilized in a manner consistent with other successful ASF projects. While incubation status is not necessarily a reflection of the completeness or stability of the code, it does indicate that the project has yet to be fully endorsed by the ASF.SINGA’s software stack includes three major components, namely, core, IO and model. Figure 1 illustrates these components together with the…

Apache SINGA

4

Microsoft Cognitive Toolkit

The Microsoft Cognitive Toolkit is a free, easy-to-use, open-source, commercial-grade toolkit that trains deep learning algorithms to learn like the human brain. The Microsoft Cognitive Toolkit—previously known as CNTK—empowers users to harness the intelligence within massive datasets through deep learning by providing uncompromised scaling, speed, and accuracy with commercial-grade quality and compatibility with the programming languages and algorithms users already use. The Microsoft Cognitive Toolkit, is a unified deep-learning toolkit that describes neural networks as a series of computational steps via a directed graph. In this directed graph, leaf nodes represent input values or network parameters, while other nodes represent…

Microsoft Cognitive Toolkit

5

Keras

Keras is a deep learning library for Theano and TensorFlow. It is a high-level neural networks library, written in Python and capable of running on top of either TensorFlow or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research. Keras deep learning library allows for easy and fast prototyping (through total modularity, minimalism, and extensibility). It supports both convolutional networks and recurrent networks, as well as combinations of the two. Keras also supports arbitrary connectivity schemes (including multi-input and…

Keras

6

Deeplearning4j

Deeplearning4j is an open source deep learning library written for Java and the Java Virtual Machine and is a computing framework with wide support for deep learning algorithms. Deeplearning4j is most helpful in solving distinct problems, like identifying faces, voices, spam or e-commerce fraud. Deeplearning4j includes implementations of the restricted Boltzmann machine, deep belief net, deep autoencoder, stacked denoising autoencoder and recursive neural tensor network, as well as word2vec. These algorithms all include distributed parallel versions. Deeplearning4j 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 on distributed GPUs and CPUs. Skymind is its commercial support arm. Deeplearning4j aims to be cutting-edge plug and play, more convention than configuration, which allows for fast prototyping for non-researchers. DL4J is customizable at scale. Released under the Apache 2.0 license, all derivatives of DL4J belong to their authors. DL4J can import neural net models from most major frameworks via Keras, including TensorFlow, Caffe, Torch and Theano, bridging the gap between the…

Deeplearning4j

7

Theano

Theano is a numerical computation library for Python, where computations are expressed using a NumPy-like syntax and compiled to run efficiently on either CPU or GPU architectures. Theano allows to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. Theano is a Python library that lets users define, optimize, and evaluate mathematical expressions, especially ones with multi-dimensional arrays (numpy.ndarray). It is possible to attain speeds rivaling hand-crafted C implementations for problems involving large amounts of data using Theano. 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 algebra 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 compilation is particularly useful for tasks in which complicated mathematical…

Theano

8

MXNet

Mxnet is a flexible and efficient library for deep learning. MXNet is developed by collaborators from multiple universities and companies. MXNet provides a rich Python API to serve a broad community of Python developers. MXNet offer powerful tools to help developers exploit the full capabilities of GPUs and cloud computing. While these tools are generally useful and applicable to any mathematical computation, MXNet places a special emphasis on speeding up the development and deployment of large-scale deep neural networks. With MXNet, it’s easy to specify where each data structures should live. MXNet makes it easy to scale computation with number…

MXNet

9

H2O.ai

H2O is an Open Source Fast Scalable Machine Learning API for Smarter Applications (Deep Learning, Gradient Boosting, Random Forest, Generalized Linear Modeling (Logistic Regression, Elastic Net), K-Means etc.H2O makes it possible for anyone to easily apply machine learning and predictive analytics to solve today’s most challenging business problems. H2O was written from scratch in Java and seamlessly integrates with the most popular open source products like Apache Hadoop and Spark to give customers the flexibility to solve their most challenging data problems. H2O’s intuitive web-based Flow graphical user interface or familiar programming environments like R, Python, Java, Scala, JSON, and…

H2O.ai

10

ConvNetJS

ConvNetJS is a Javascript library for training Deep Learning models (Neural Networks) entirely in users’ browsers. Users just open a tab and they are training. No software requirements, no compilers, no installations, no GPUs, no sweat. The library allows users to formulate and solve Neural Networks in Javascript, and was originally written by @karpathy (a PhD student at Stanford). However, the library has since been extended by contributions from the community. The code is available on Github under MIT license. Pull requests for new features / layers / demos and miscellaneous improvements are encouraged. The library is also available on…

ConvNetJS

11

DeepLearningKit

DeepLearningKit is an Open Source with Apache 2.0 License. It is a Deep Learning Framework for Apple’s iOS, OS X and tvOS that is available at github.com/DeepLearningKit/DeepLearningKit. The goal is to support using pre-trained Deep Learning models on all Apple’s devices that have GPU(s). It is developed in Swift to easily run on all platforms such as iOS, OS X and tvOS and Metal to efficiently use on-device GPU to ensure low-latency Deep Learning calculations.DeepLearningKit currently supports using (Deep) Convolutional Neural Networks, such as for image recognition, trained with the Caffe Deep Learning Framework but the long term goal is…

DeepLearningKit

12

Gensim

Gensim is an open source vector space modeling and topic modeling toolkit, implemented in the Python programming language intended for handling large text collections, using efficient online algorithms.Gensim includes implementations of tf–idf, random projections, deep learning with Google's word2vec algorithm , hierarchical Dirichlet processes (HDP), latent semantic analysis (LSA) and latent Dirichlet allocation (LDA), including distributed parallel versions. Gensim is a FREE Python library that has scalable statistical semantics. It analyzes plain-text documents for semantic structure and retrieve semantically similar documents. In addition, Gensim is a robust, efficient and hassle-free piece of software to realize unsupervised semantic modelling from plain text. It stands in contrast to brittle homework-assignment-implementations that do not scale on one hand, and robust java-esque projects that take forever just to run “hello world”. Gensim is licensed under the OSI-approved GNU LGPLv2.1 license. This means that it’s free for both personal and commercial use, but if users make any modification to gensim that users distribute…

Gensim

13

Caffe

Caffe is a deep learning framework developed with cleanliness, readability, and speed. The clean architecture enables rapid deployment. The Networks are specified in simple config files, with no hard-coded parameters in the code. Switching between CPU and GPU in Caffe is as simple as setting a flag and hence the models can be trained on a GPU machine, and then used on commodity clusters. Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (BAIR) and by community contributors. Yangqing Jia created the project during his PhD at UC Berkeley. Caffe is released under the BSD 2-Clause license. The BAIR/BVLC reference models are released for unrestricted use. Caffe is installed and run on Ubuntu 16.04–12.04, OS X 10.11–10.8, and through Docker and AWS. Caffe requires the CUDA nvcc compiler to compile its GPU code and CUDA driver for GPU operation. Caffe promotes expressive architecture which encourages application and innovation. Models and optimization are…

Caffe

14

ND4J

ND4J is a free, open source extension of the Java programming language operating on the Java Virtual Machine. It is a scientific computing library for linear algebra and matrix manipulation in a production environment. ND4J is a scientific computing libraries for the JVM. It is meant to be used in production environments, which means routines are designed to run fast with minimum RAM requirements. ND4J is used by national laboratories for tasks such as climatic modeling, which require computationally intensive simulations. ND4J brings the intuitive scientific computing tools of the Python community to the JVM in an open source, distributed and GPU-enabled library. In structure, it is similar to SLF4J. ND4J gives engineers in production environments an easy way to port their algorithms and interface with other libraries in the Java and Scala ecosystems.…

ND4J

15.DeepLearnToolbox

DeepLearnToolbox is a Matlab/Octave toolbox for deep learning and includes Deep Belief Nets, Stacked Autoencoders, Convolutional Neural Nets, Convolutional Autoencoders and vanilla Neural Nets.

DeepLearnToolbox

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