5 Free Deep Learning Tools You Must Know About

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Deep Learning Tools
Courtesy [6]

Deep learning is a one of the best Machine learning technique. It is being highly explored and researched by big companies and researchers. Companies and researchers have developed 50+ tools and framework for Deep Learning to accelerate research and development. This article gives details of five Free Open Source Deep Learning Tools.

Deep Learning Tools and Frameworks

1. TensorFlow 

TensorFlow is an Machine Intelligence software library developed by Google Brain Team. Different Deep learning algorithms can be implemented using the library. It is developed using C++ and Python. It is released under Apache 2.0 software license [1].

Key Features

  • True Portability: Runs on CPUs or GPUs, and on desktop, server, or mobile computing platforms.
  • Gradient based machine learning algorithms with automatic differentiation capabilities
  • Support for threads, queues, and asynchronous computation
  • One can use it if he/she can express computation as a data flow graph
  • Reccurent Neural Net, Convolutional Neural Net, RBM / DBSs Neural Net
  • Python, C/C++ public API Interface
  • Platforms supported: Linux, Mac OS X, Windows.

2. Caffe

It is an open source deep learning framework released under BSD 2 license. Berkeley Vision and Learning Center and community contributors have developed it. It is developed in c++ [2] .

Key Features

  • It is modular.
  • Fast.
  • Reccurent Neural Net and Convolutional Neural Net
  • Provides interface with C++, Command line, Python, MATLAB.
  • It has support for CUDA.
  • Platforms supported: Linux, Android, Mac OS X, iOS, Windows.

3. MXNet

It is scalable open source Deep Learning Framework developed by collaborators from multiple universities and companies. It has been developed using Small C++ core library. Released under open souce apache 2.0 license [3].

Key Features

  • Fast, light weight and Conscise
  • Distributed Deep Learning
  • Provides interface with C++, Python, Julia, Matlab, JavaScript, Go, R, Scala
  • Easily extensible C++/CUDA neural network toolkit
  • Reccurent Neural Net, Convolutional Neural Net, RBM / DBSs Neural Net
  • Platforms Supported : Ubuntu, OS X, Windows, AWS, Android, iOS

4. Microsoft Cognitive Toolkit

Microsoft Cognitive Toolkit (CNTK) is an open source Toolkit developed by Microsoft Research. It is developed using C++. It is released under MIT software license [4].

Key Features

  • Parallelism with accuracy on multiple GPUs/machines via 1-bit SGD and Block Momentum
  • Memory sharing and other built-in methods to fit even the largest models in GPU memory
  • Full APIs for defining networks, learners, readers, training and evaluation from Python, C++ and BrainScript
  • Reinforcement learning, generative adversarial networks, supervised and unsupervised learning
  • Ability to add new user-defined core-components on the GPU from Python
  • Both high-level and low-level APIs available for ease of use and flexibility
  • Takes advantage of high-speed resources when used with Azure GPU and Azure networks
  • Reccurent Neural Net and Convolutional Neural Net
  • Interface support with Python, C++, Command line, BrainScript, .NET
  • Platforms Supported:Windows, Linux (OSX via Docker on roadmap)

5. Torch

Torch is an open source scientific computing framework with support for Deep learning. Implemented using fast scripting language, LuaJIT, and an underlying C/CUDA. It is released under BSD License [5].

Key Features

  • A powerful N-dimensional array
  • Lots of routines for indexing, slicing, transposing, …
  • Numeric optimization routines
  • Fast and efficient GPU support
  • Embeddable, with ports to iOS, Android and FPGA backends
  • Reccurent Neural Net, Convolutional Neural Net, RBM / DBSs Neural Net
  • Provides Interface for Lua, C, utility library for C++/OpenCL
  • Supported Platforms: Linux, Android, Mac OS X, iOS, Windows

References

1. https://www.tensorflow.org/
2. https://en.wikipedia.org/wiki/Comparison_of_deep_learning_software
3. http://mxnet.io/
4. https://www.microsoft.com/en-us/research/product/cognitive-toolkit/
5. Torch.co
6. http://iitk.ac.in/ee/DeepLearning_ShortTermCourse/

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Dr R M Makwana
Dr. Makwana is Ph.D. in Computer Engineering, specialized in Artificial Intelligence from Sardar Patel University, Anand, Gujarat, India. Accelerated career growth from lecturer to professor in short span, having teaching experience of more than 13 years. He is TechSavvy with Research interest in Artificial Intelligence, Image Processing, Computer Vision, and Internet of Things. Actively supporting research community by providing service as a member of technical program committees of national and international conferences and workshops, as well as by reviewing journal and conference papers.

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