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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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Artificial intelligence has transformed the way business are done in the world today. Business leaders need to keep pace with the latest business and artificial intelligence to improve their performance and their businesses. Business leaders need to embrace systems that can help them to solve their day to day problems. Companies are thrilled by the phrase “big data” as there is value in collecting data around business processes. Different businesses especially those involved in the data business, for example, Google, Facebook, Amazon, Netflix and more need a system that can help them not only collect data but also make better predictions to increase their profits. They also need sophisticated ways to query and analyze that data.  Deep learning is definitely the way to go. So what is deep learning?

Deep learning is an aspect of Artificial Intelligence that is concerned with how computers learn through the approach that human beings use to obtain certain kinds of knowledge as opposed to what human beings program it to do. Deep learning is a set of algorithms that are used in machine learning and the learning occurs unsupervised. Machine learning helps businesses develop models that are more predictive in terms of outcome and that can help businesses make better decisions.

Businesses can use machine learning to win new customers, analyze products and automate things. Traditional machine learning is linear whereas deep learning algorithms are heaped in layers of non-linear transformation and its input increase in complexity and abstraction are used in a statistical model as the output. The output level of accuracy is achieved as iterations continue. It mimics the human neurons system and is thus sometimes referred to as deep neural networking. The machine is exposed to huge amounts of training data and processing power to achieve an acceptable level of accuracy.

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

What is 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.

What is Deep Learning Software?

What is Deep Learning Software?

  • Convolutional neural networks: Convolutional neural networks involve the use of deep artificial neural networks to analyze visual imagery. It helps cluster images by similarity and do image recognition within scenes. The algorithms help recognize faces, individuals, street signs, tumors, and more.
  • Document classification: Deep learning enables document classification algorithmically where task involves assigning a document to one or several classes which makes it easy to sort and manage. The documents categorized may be in form of images, texts, music etc
  • Image segmentation: Another feature of deep learning involves image segmentation that involves division of an image into separate pieces that cover it. It helps is to change the image representation into something that is easier to analyze and that has meaning.
  • ML algorithm library: Deep learning is an open source of Machine learning algorithm library for everyone.
  • Model training : Deep learning helps in model training that involves providing machine learning algorithm with training data to learn from.
  • Neural network modeling: Another feature of deep learning s neural networking modeling that involves the use of artificial neural networks to forecast and predict outcomes based on simple mathematical models.
  • Self-learning: Deep learning involves self and unsupervised feature learning.
  • Visualization: Visualization is another feature of deep learning that entails the ability to represent data in images, diagrams or animations to communicate a message.

 Top Deep Learning Software

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

Neural Designer

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…

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Neural Designer is a professional application for discovering complex relationships, recognizing unknown patterns and predicting actual trends from data sets by means of neural networks. Some of the examples where Neural Designer has used are in flight data to increase comfort and reduce consumption of aircrafts, in medical databases to make more reliable and less invasive diagnosis. Neural Designer has also used in physico-chemical data to increase the quality of wines and in sales data to optimize provisioning and to improve work quadrants.

9.5
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7.9
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Neural Designer

Neural Viewer

2

H2O.ai

H2O

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…

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H2O makes it possible for anyone to easily apply machine learning and predictive analytics to solve today’s most challenging business problems.

7.6
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7.0
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H2O.ai

3

DeepLearningKit

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…

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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 to support using deep learning models trained with the most popular Deep Learning frameworks such as TensorFlow and Torch.

7.5
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4

Microsoft Cognitive Toolkit

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…

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The Microsoft Cognitive Toolkit is built with sophisticated algorithms and production readers to work reliably with massive datasets. Skype, Cortana, Bing, Xbox, and industry-leading data scientists already use the Microsoft Cognitive Toolkit to develop commercial-grade AI.

7.9
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0.0
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Microsoft Cognitive Toolkit

5

Keras

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…

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Keras allows for easy and fast prototyping (through total modularity, minimalism, and extensibility), supports both convolutional networks and recurrent networks, as well as combinations of the two and supports arbitrary connectivity schemes (including multi-input and multi-output training).

7.6
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8.9
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Keras

6

ConvNetJS

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…

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The library allows you to formulate and solve Neural Networks in Javascript. Current support includes Common Neural Network modules (fully connected layers, non-linearities), Classification (SVM/Softmax) and Regression (L2) cost functions, Ability to specify and train Convolutional Networks that process images and An experimental Reinforcement Learning module, based on Deep Q Learning.

7.6
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6.0
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ConvNetJS

7

Torch

Torch

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…

Bottom Line

Torch is a popular neural network and optimization libraries which offers simple to use function for its users while having maximum flexibility in implementing complex neural network topologies. Users can build arbitrary graphs of neural networks and parallelize them over CPUs and GPUs in an efficient manner.

7.5
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0.2
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8

Gensim

Gensim

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…

7.6
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9

Deeplearning4j

Deeplearning4j

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…

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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.

7.8
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Deeplearning4j

10

Apache SINGA

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…

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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.

7.9
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9.7
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Apache SINGA

11

Caffe

Caffe

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…

Bottom Line

Expressive architecture encourages application and innovation. Models and optimization are defined by configuration without hard-coding. Switch between CPU and GPU by setting a single flag to train on a GPU machine then deploy to commodity clusters or mobile devices.

7.6
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12

Theano

Theano

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…

Bottom Line

Theano is a Python library that lets you to define, optimize, and evaluate mathematical expressions, especially ones with multi-dimensional arrays (numpy.ndarray). Using Theano it is possible to attain speeds rivaling hand-crafted C implementations for problems involving large amounts of data.

7.8
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13

ND4J

ND4J

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.…

Bottom Line

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.

7.6
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14

MXNet

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…

Bottom Line

MXNet provides a rich Python API to serve a broad community of Python developers.

7.8
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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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