Did you know that artificial intelligence will become stronger than human intelligence by 2029? Gartner forecast says so. Well, we think that this fact is not far from the truth. Chatbots and virtual assistants in our mobile devices become smarter and more powerful; they may help solve many issues.
That is why software developers integrated AI into a large number of apps. There is a wide range of AI-based tools, and today, we will single out ten main AI frameworks and libraries that are used by many software developers to enhance new projects. Want to know them? Let’s go.
Frameworks and libs with machine learning
We will start with ML-based libs and frameworks, and we will list the most popular ones.
Azure Machine Learning Studio
It is a cloud solution for developers where they can make use of complex ML models faster and easier. AMLS is a Microsoft product, and it is considered to be one of the handiest tools used for machine learning integration. Develops may create solutions for service improvement, fraud prevention, and implementation of other tasks.
Its benefits include easy access to the product, intuitive environment for collaboration with other devs, it allows developers to implement recurrent use of models, use various modeling strategies easily.
IBM Watson
It is a large system that is used in many fields of activity. This system is capable of learning, understand and draw conclusions. IBM Watson is used for epidemics forecasting, atmosphere condition monitoring, business processes optimizations. Also, AI in logistics can be successfully applied due to Watson. It provides devs with cloud API platform to use it during web and mobile apps development. In total, Watson offers about 50 APIs at the moment, so developers may use chosen APIs for their purposes.
Its benefits include diversification in usage. For example, scientists use it for earthquakes forecasting, whereas business people apply it for trends prediction and market analysis. Also, Watson is used in the medical area for diagnosing diseases. Thus, the capabilities of Watson are really awesome and it can improve any software.
PyBrain
It is a Python library that helps work with neural networks. It consists of a large number of ML algorithms, and also it an easy-to-use and clear library.
Its benefits include one universal environment for algorithms implementation, you don’t need to use many libraries. It is good for the solving of complex math tasks, and this library is open-source which is a very attractive option for developers.
TensorFlow
This is a neural network library built by Google. It can solve various tasks by processing a huge amount of data to get the correct result. Apart from this, TensorFlow has APIs for C languages and Python, that makes its library much more attractive for other developers.
Its benefits include processing of huge amount of data, TensorFlow is open-source, you can create AI-based networks in pattern recognition. Also, TF is used for deep learning.
Torch
This library is used for scientific calculations, it includes various modules set, and each module can be used for integration of a specific stage of the neural network into a software. For instance, one module can be used for configuration of the neural network, whereas another module is used for learning process optimization.
Its benefits include a capability to build complex ML networks with the help of containers mechanism. Container combines components of neural networks into one configuration.
Frameworks and libs with deep learning
If somebody doesn’t know, deep learning is a subset of ML. Deep learning is used to make software learn the way how humans make decisions. It is a more complex type of learning. So let’s study the main tools which include deep learning.
Caffe
This framework offers a wide range of tools for deep learning algorithms integration into any software. It is used for image and speech recognition, and it is a really powerful DL framework.
Its benefits include clean architecture for quick integration, open-source code, so developers may modify the framework according to their needs. Also, it is a high-performance framework.
DeepLearning4j
A distributed library with open-source code. It is built specifically for Java and Scala languages. Java Virtual Machine is the foundation where DL4j is functioning.
Its benefits include high performance it can process a large amount of data. Also, developers can implement deep learning algorithms from scratch.
Keras
Keras library allows developers to work with neural networks on a high level, it helps simplify many tasks related to math calculations. It is used on convolutional and recurrent neural networks.
Its benefits include clear structure, especially for devs which are skilled in ML, open-source code, an intuitive scheme of implementation.
Swift AI
This library is built only for Mac computers and for Swift language particularly. Devs can build neural networks using deep learning algorithms, and it used more for written speech recognition.
Its benefits include signal processing implementation, abundant toolkit, it makes uses of all technical particularities of Apple device, used on convolutional and recurrent neural networks.
Theano
Extension and library of Python, it is targeted at the calculation of mathematical expressions. It includes a compiler that converts math expressions in Python to C language code.
Its benefits include a special toolkit to configure neural networks, autocoders, convolutional and recurrent neural networks.
We hope this list will help you find the right tool for your software and make it really amazing with AI capabilities. Just follow the right road!
Note: This is a guest post, and opinion in this article is of the guest writer. If you have any issues with any of the articles posted at www.marktechpost.com please contact at asif@marktechpost.com
Vitaly Kuprenko is a technical writer at Cleveroad It's a mobile app and web development company in Ukraine. He enjoys telling about tech innovations and digital ways to boost businesses.
