The existence of Artificial intelligence has been for a long time in the industry. The impact of AI has never been limited moreover, it has become an important element in recent years.
The huge improvements in various fields, especially in mobile App development, is due to the development of various libraries and framework. Thus we can say that AI has become a responsive IT field and has lots of research going into it. There are many AI solutions that came into existence due to the implementation and integration of libraries and framework.
Let’s discuss best framework for AI in the market
1. TensorFlow
TensorFlow is a neural network library created by Google. Moreover, it is an open-source software for carrying out numerical computations using data flow graphs.
Talking about the working it learns to solve tasks by enhancing and processing of data in different nodes. The focus of the whole process is to find a correct solution. Tensor is available in the Python programming language.
Apart from that, it offers developers to use APIs for Python and C/C++ languages that can connect to the developer’s software. Moreover, this framework is acknowledged for having a strong architecture.
Therefore, a framework with such architecture allows computation on any CPU or GPU, whether be it a mobile device, a desktop or a server.
Advantages:
• The library is open-source therefore data processing in a cheap cloud environment;
• It is good for deep learning and it builds AI networks in pattern recognition.
2. Theano
Theano is a powerful Python library and is considered as a strong competitor to TensorFlow. The respective library’s transparent use of a GPU for carrying out data-intensive computations. Moreover, it is built for numerical operations involving multi-dimensional arrays with a high level of efficiency.
Theano is a library and extension of Python language that allows scientists to calculate efficiently mathematical expressions. Theano is used in powering large-scale computationally intensive operations for fruitful results.
The best part of this library that it is also developed for boosting a quick machine learning development. Hence, its compiler converts the mathematical expressions written in Python language to C or CUDA code.
Advantages:
• Toolkit is helpful for neural networks configuration and their learning.
• Extremely easy-to-use that can be easily edited using Python.
• Execution of multi-layer perceptrons, recurrent neural networks, autoencoders and convolutional etc.
3. Caffe
When we talk about Caffe, with it we can very easily build a Convolutional Neural Network (CNN). Initially, Caffe framework is created for commercial use only. Its major work is used for image classification. When it works well on GPU, contributes great speed during operations.
It is an open-source, written in C++ language and apart from that it allows to write user algorithms in Python. Therefore, it offers a wide toolkit for the development and deployment of modern deep learning algorithms.
Fields like astronomy and robotics are evolving with the use of speech and images recognition. The solution provided is provided to different fields. High performance makes Caffe a perfect tool among all. Today Caffe is the leader in the top of artificial intelligence framework for deep learning for commercial use.
Like the other frameworks, there are available models of learning that are already integrated into the system efficient for AI and deep learning research.
Advantages:
· Clean architecture for instant deployment at the time of process.
· it performs easily and quickly at the time of switching between central and graphics processing units.
· It is an Open-source code that allows developers to control integration and also modify it for their needs.
4. Keras
Keras characterizes the library that is an open-source neural network library written in Python. Moreover, it can work with neural networks on a higher level.
It simplifies many tasks but is not meant to be an end-to-end machine learning framework like others on the list.
It is used on recurrent neural networks and convolutional on CPU and GPU. Therefore, provides a high level of abstraction, which makes for easy configuration of neural networks regardless of the framework it is sitting on. The best part about Keras is that Google’s TensorFlow currently supports Keras as a backend.
Advantages:
· It is Easy experimentation implementation and easy-to-use.
· Open-source code that is absolutely clear for developers experienced in machine learning.
5. CNTK
CNTK is a Computational Network Toolkit from Microsoft. The toolkit is used in Microsoft products such as Windows Cortana, Skype Translator where speech recognition services are utilized.
CNTK is developed in C++ language and can be used for automated translation and image recognition tasks resolution.
Microsoft’s CNTK (Computational Network Toolkit) is a library that enhances the modularization thus providing learning algorithms and model descriptions. Moreover, it works with the maintenance of the separation of computation networks.
The process of CNTK can utilize many servers at the same time. Therefore, CNTK allows developers to create distributed neural networks made in a case where lots of servers are needed for operations.
Moreover, its functionality is considered to be close to Google’s TensorFlow. However, little speedy than TensorFlow.
Advantages:
· CNTK supports different models of neural networks.
· It also supports feed-forward, convolutional, recurrent neural networks as well as their combinations.
· Mostly use GPU for calculations therefore provides linear scalability.
Wrapping It Up
The libraries discussed have proven over time to be of high quality solution in different industry. Giants like Google, Yahoo, Apple, and Microsoft make use of some of these libraries for their deep learning and machine learning projects. These frameworks are proving to be the best tools in App development market where developers are developing new solution for their clients.
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Originally published at www.bebee.com.