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Best Deep Learning Tools | Deep Learning Software Vendors List

This page covers deep learning tools and softwares available across the world. It provides list of best deep learning tools and popular deep learning software vendors.

What is Deep Learning?

Introduction:
• Machine Learning extracts the features of images such as corners and edges in order to create models of the various objects. It later uses these models to identify the objects. Machine learning does not require high performance processors and more data.
• Deep Learning is subtype of machine learning. Deep learning is a machine learning technique which learns features and tasks directly from data. The data can be images, text files or sound.

difference between machine learning and deep learning
Figure-1: Machine Learning Vs Deep Learning

Feature extraction and classification are carried out by deep learning algorithms known as convolutional neural network (CNN). Deep Learning works well with lots of data and using high performance GPUs. Refer Deep Learning Advantages and Disadvantages >> for more information.

List of Best Deep Learning Tools | Deep Learning Software Vendors

Following table-1 mentions list of best deep learning tools. The right Deep Learning tool selection depends on two factors viz. type of problem required to be solved and type of data required to be handled. This list of deep learning software vendors or developers information is very useful to select the right tool or software for your intended application or use.


Deep Learning Tools Description with features
TensorFlow It consists of two tools viz. TensorBoard (For Data Visualization) and TensorFlow Serving (For new algorithm development) .
PyTorch It runs on Python. It employs CUDA along with C and C++ libraries.
Keras Its library supports both convolution and re-current networks. It has been integrated with TensorFlow. Its main uses are classification, tagging, translation, speech recognition and text generation and summarization,
Theano Developed by MILA lab at university of Montreal. It is CPU/GPU symbolic expression compiler in python.
Pylearn2 It is a library which is designed to perform research in machine learning domain.
Caffe It is framework with expression, speed and modularity in mind. It has been developed by BAIR (Berkeley AI Research) and other community contributors. It has been released under BSD 2-clause license.
MXNet It is rich in language bindings. It works with Python, Scala, R, Perl, Julia, Matlab, C++ and Javascript etc. It is highly scalable tool which can be used for wide variety of devices.
H2O It was developed by Mountain View company (Now known as H20.ai). It has released under Apache 2.0 open source license. H20 software was written in Java, Python and R.
Microsoft Cognitive Toolkit (Previously Known as CNTK) Its components can handle multi-dimensional dense or sparse data from Python, C++ or BrainScript. It is deep learning framework developed by Microsoft Research.
Torch Torch is an open-source machine learning library. It is a scientific computing framework. It is a script language based on Lua programming language. It provides wide range of algorithms for deep learning. It uses scripting language LuaJIT with underlying 'C' implementation.
Chainer It is an open source deep learning framework. It is written purely in Python on top of Numpy and CuPy Python libraries. The development is led by Japanese venture company "Preferred Networks" in partnership with IBM, Intel, Microsoft, and Nvidia.
Neural Designer It is used for data analytics with GUI for easier data entry and intrepretation of results. It is written in C++ by Artificial Intelligence Techniques, Ltd.
BigDL It is a distributed deep learning framework for Apache Spark. It has been created by Jason Dai at Intel. It is hosted at GitHub.
OpenNN It is a library which is written using C++. It implements neural networks. This library is a open-source and licensed under the GNU.
RapidMiner software It provides an integrated environment for data preparation, machine learning, deep learning, text mining, and predictive analytics. It uses Java.
XGBoost software It is an open-source software library which provides a gradient boosting framework for C++, Java, Python, R, and Julia. It works on Linux, Windows, and macOS.

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