The list of the library of Artificial intelligence


1. Tensorflow provide by Google
2. OpenCV library
3. Computational Network Toolkit provide by Microsoft
4. Amazon Dsstne provide by the developer in Amazon
5. Deep Text provide by Facebook
Summary:
I. Tensorflow is the Open Source Library for AI .Google used Tensorflow to support of their products such as YouTube Google Photos Google Translate Google search engine. It supports three programming languages like Python Java and C++.
II. OpenCV Library is released under a BSD license and hence it’s free for both academic and commercial use. OpenCV well know using for Video Analysis, Object detection, Image Processing. OpenCV supports many languages like Java, Python, C, C++, C#.
III. CNTK is the Computational Network Toolkit by Microsoft Research, is a unified deep-learning toolkit that describes neural networks as a series of computational steps via a directed graph. The combination of CNTK and Azure GPU Lab allows us to build and train deep neural nets for Cortana speech recognition up to 10 times faster than our previous deep learning system.
IV. Amazon DSSTNE (pronounced "Destiny") is an open source software library for training and deploying deep neural networks using GPUs. Amazon engineers built DSSTNE to solve deep learning problems at Amazon's scale.
V. Deep Text is an approach to text analytics that adds depth and intelligence to our ability to utilize a growing mass of unstructured text the world is drowning in. Deep Text used in Facebook website, Messenger.
Conclusion, all libraries above are very useful for Machine learning and Artificial intelligence. It has different functionality base on our requirement.

Details
I. Tensorflow(Open source )
Tensorflow is the open source library for Artificial intelligence numerical computation using data flow graphs.

The things that we can do with Tensorflow:
Image Processing
Image Recognition ,Language and Sequence Processing
Neural Networks
TensorFlow  Mechanics 101 (to train and evaluate a simple feed-forward neural network for handwritten digit classification using the (classic) MNIST data set)
TensorFlow Wide & Deep Learning (trained a logistic regression model to predict the probability.)
Speech Recognition.

TensorFlow Features:
o Deep Flexibility: TensorFlow isn't a rigid neural networks library.
o True Portability: TensorFlow runs on CPUs or GPUs, and on desktop, server, or mobile computing platforms. Want to play around with a machine learning idea on your laptop.
o Connect Research and Production
o Auto-Differentiation: Gradient based machine learning algorithms will benefit from TensorFlow's automatic differentiation capabilities.
o Language Options:  Tensorflow support Three Programming languages such as C++, Python, and Java.
o Maximize Performance: use every ounce of muscle in that workstation with 32 CPU cores and 4 GPU.

The popular in Github:
II. OpenCv Library (Open source)
OpenCv Library is released under a BSD license and hence it’s free for both academic and commercial use.

The things that we can do with OpenCV:
Image Processing
High-level GUI and Media I/O
Video Analysis
Object Detection
Machine Learning
3D Visualizer
OpenCV features:
Support many programming languages like Java, Python, C, and C++ …
Support many OS like Window OS, Mac OS, Android, IOS, Linux, and Ubuntu...
The library can take advantage of multi-core processing.
It can take advantage of the hardware acceleration of the underlying heterogeneous compute platform.
Adopted all around the world.
OpenCV has more than 47 thousand people of user community.
The Popular in GitHub:

III. Computation Network Toolkit (Open source)
CNTK is the Computational Network Toolkit by Microsoft Research, is a unified deep-learning toolkit that describes neural networks as a series of computational steps via a directed graph.

  The things that we can do with CNTK:
1. The combination of CNTK and Azure GPU Lab allows us to build and train deep neural nets for Cortana speech recognition up to 10 times faster than our previous deep learning system.
2. Speech recognition.
3. Deep learning computational performance


CNTK features:
• CPU and GPU with a focus on GPU Cluster
• Windows and Linux
• Automatic numerical differentiation
• Memory sharing during execution planning
             Modularized: separation of
• Computational networks
• Execution engine
Learning algorithms
              Model description
• Data readers
• Models can be described and modified with
• C++ code
• Network definition language (NDL) and model editing      
• Python and C# (planned)

The popular on GitHub



IV. Amazon DSSTNE (Open source)
Amazon DSSTNE (pronounced "Destiny") is an open source software library for training and deploying deep neural networks using GPUs. Amazon engineers built DSSTNE to solve deep learning problems at Amazon's scale.


DSSTNE was built with a number of features for production workloads:
Multi-GPU Scale
Large Layers
Sparse Data
The popular in Github :


V. Deep Text (Not Open sources)
o Deep Text is an approach to text analytics that adds depth and intelligence to our ability to utilize a growing mass of unstructured text the world is drowning in.
o DeepText leverages several deep neural network architectures, including convolutional and recurrent neural nets, and can perform word-level and character-level based learning.
o DeepText is used  to help it make sense of the mountains of unstructured data on the social network.

Facebook of using Deep Text
Facebook engineers can easily build new DeepText models through the self-serve architecture that DeepText provides.
Facebook's midterm and longer-term plans are to use artificial intelligence to enhance its core ecosystem and branch out into new ventures.
Facebook announced Deep Text, an A.I. engine to understand the meaning and sentiment behind all of the text posted by users.  It actually has the potential to transform the social network into a powerful search engine.
Facebook is sitting on a mountain of information that it can use to connect people with similar interests, sell more ads, and help people find things. For example if someone texts, “I need a ride” to someone else a bot could interject to ask whether it should call them a taxi.

The list of the library of Artificial intelligence:
1. Tensorflow provide by Google
2. OpenCV library
3. Computational Network Toolkit provide by Microsoft
4. Amazon Dsstne provide by the developer in Amazon
5. Deep Text provide by Facebook
Summary:
I. Tensorflow is the Open Source Library for AI .Google used Tensorflow to support of their products such as YouTube Google Photos Google Translate Google search engine. It supports three programming languages like Python Java and C++.
II. OpenCV Library is released under a BSD license and hence it’s free for both academic and commercial use. OpenCV well know using for Video Analysis, Object detection, Image Processing. OpenCV supports many languages like Java, Python, C, C++, C#.
III. CNTK is the Computational Network Toolkit by Microsoft Research, is a unified deep-learning toolkit that describes neural networks as a series of computational steps via a directed graph. The combination of CNTK and Azure GPU Lab allows us to build and train deep neural nets for Cortana speech recognition up to 10 times faster than our previous deep learning system.
IV. Amazon DSSTNE (pronounced "Destiny") is an open source software library for training and deploying deep neural networks using GPUs. Amazon engineers built DSSTNE to solve deep learning problems at Amazon's scale.
V. Deep Text is an approach to text analytics that adds depth and intelligence to our ability to utilize a growing mass of unstructured text the world is drowning in. Deep Text used in Facebook website, Messenger.
Conclusion, all libraries above are very useful for Machine learning and Artificial intelligence. It has different functionality base on our requirement.

Details
I. Tensorflow(Open source )
Tensorflow is the open source library for Artificial intelligence numerical computation using data flow graphs.

The things that we can do with Tensorflow:
Image Processing
Image Recognition ,Language and Sequence Processing
Neural Networks
TensorFlow  Mechanics 101 (to train and evaluate a simple feed-forward neural network for handwritten digit classification using the (classic) MNIST data set)
TensorFlow Wide & Deep Learning (trained a logistic regression model to predict the probability.)
Speech Recognition.

TensorFlow Features:
o Deep Flexibility: TensorFlow isn't a rigid neural networks library.
o True Portability: TensorFlow runs on CPUs or GPUs, and on desktop, server, or mobile computing platforms. Want to play around with a machine learning idea on your laptop.
o Connect Research and Production
o Auto-Differentiation: Gradient based machine learning algorithms will benefit from TensorFlow's automatic differentiation capabilities.
o Language Options:  Tensorflow support Three Programming languages such as C++, Python, and Java.
o Maximize Performance: use every ounce of muscle in that workstation with 32 CPU cores and 4 GPU.

The popular in Github:
II. OpenCv Library (Open source)
OpenCv Library is released under a BSD license and hence it’s free for both academic and commercial use.

The things that we can do with OpenCV:
Image Processing
High-level GUI and Media I/O
Video Analysis
Object Detection
Machine Learning
3D Visualizer
OpenCV features:
Support many programming languages like Java, Python, C, and C++ …
Support many OS like Window OS, Mac OS, Android, IOS, Linux, and Ubuntu...
The library can take advantage of multi-core processing.
It can take advantage of the hardware acceleration of the underlying heterogeneous compute platform.
Adopted all around the world.
OpenCV has more than 47 thousand people of user community.
The Popular in GitHub:

III. Computation Network Toolkit (Open source)
CNTK is the Computational Network Toolkit by Microsoft Research, is a unified deep-learning toolkit that describes neural networks as a series of computational steps via a directed graph.

  The things that we can do with CNTK:
1. The combination of CNTK and Azure GPU Lab allows us to build and train deep neural nets for Cortana speech recognition up to 10 times faster than our previous deep learning system.
2. Speech recognition.
3. Deep learning computational performance


CNTK features:
• CPU and GPU with a focus on GPU Cluster
• Windows and Linux
• Automatic numerical differentiation
• Memory sharing during execution planning
             Modularized: separation of
• Computational networks
• Execution engine
Learning algorithms
              Model description
• Data readers
• Models can be described and modified with
• C++ code
• Network definition language (NDL) and model editing      
• Python and C# (planned)

The popular on GitHub



IV. Amazon DSSTNE (Open source)
Amazon DSSTNE (pronounced "Destiny") is an open source software library for training and deploying deep neural networks using GPUs. Amazon engineers built DSSTNE to solve deep learning problems at Amazon's scale.


DSSTNE was built with a number of features for production workloads:
Multi-GPU Scale
Large Layers
Sparse Data
The popular in Github :


V. Deep Text (Not Open sources)
o Deep Text is an approach to text analytics that adds depth and intelligence to our ability to utilize a growing mass of unstructured text the world is drowning in.
o DeepText leverages several deep neural network architectures, including convolutional and recurrent neural nets, and can perform word-level and character-level based learning.
o DeepText is used  to help it make sense of the mountains of unstructured data on the social network.

Facebook of using Deep Text
Facebook engineers can easily build new DeepText models through the self-serve architecture that DeepText provides.
Facebook's midterm and longer-term plans are to use artificial intelligence to enhance its core ecosystem and branch out into new ventures.
Facebook announced Deep Text, an A.I. engine to understand the meaning and sentiment behind all of the text posted by users.  It actually has the potential to transform the social network into a powerful search engine.
Facebook is sitting on a mountain of information that it can use to connect people with similar interests, sell more ads, and help people find things. For example if someone texts, “I need a ride” to someone else a bot could interject to ask whether it should call them a taxi.





The Feature of Deep Text:
- Understanding more languages faster
- Deeper understanding
- Labeled data scarcity
- Exploring Deep Text on Facebook
- Better understanding people’s interests
- Joint understanding of textual and visual content
- New deep neural network architectures
The Feature of Deep Text:
- Understanding more languages faster
- Deeper understanding
- Labeled data scarcity
- Exploring Deep Text on Facebook
- Better understanding people’s interests
- Joint understanding of textual and visual content
- New deep neural network architectures











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