Artificial intelligence tools
Currently, TensorFlow is the most sought after deep learning library. This machine learning framework by Google is a Python-friendly open source library. It facilitates numerical computation making future predictions much easier and accurate. But how?
Instead of getting entangled in the nitty-gritties of algorithms, developers can focus on the logic part of the application. TensorFlow takes care of everything that goes on the back end. The tool allows developers to construct neural network and create graphical visualisation using Tensorboard. TensorFlow applications can be run conveniently on your local machine, cloud, Android and iOS devices. As it is built on deployable scale, it runs on CPU AND GPU.
Next in competition is PyTorch, which is also built on Python. This is similar to TensorFlow in terms of nature of projects chosen. However, when the priority is for faster development, PyTorch is the better choice. TensorFlow is gone for in case the project involves larger and more complex projects.
This is a Microsoft Cognitive Toolkit, that is also built on similar lines as TensorFlow, but not as easy to deploy. It has a broader range of APIs such as Python, Java, C, C++ and mainly focuses on creating deep learning neural networks.
This open source, developed at University of California, has a Python interface. It has its best application in academic research projects and industrial disposition. This is attributed to its processing power that exceeds 60 million images per day.
5. Apache MXNet
This artificial intelligence tool is adopted by Amazon as its deep learning framework on AWS. Unlike other tools, this is not directly owned by a major corporation, which provides a conducive environment for an open source framework.
This is a high level open source neural network library which has Python interface. This extremely user-friendly tool is built on top of TensorFlow and is comparatively easier to use as well. It is used for fast prototyping that facilitates completion of state-of-the-art experiments from start to end with little or no delay. Keras run seamlessly on CPU and GPU.
As the back end is dealt by the tool itself, it attracts developers from wide range of backgrounds to get their hands on to create their own scripts, putting no limitation on skills for using the tool. So, it all comes down to your intentions. If what you need is to create a functioning prototype, Keras is your call. Else if you need to get into the low level computations of it, then TensorFlow is your way forward.
Open Neural Networks library is another open source library, that is used to simulate neural networks and hence an important component of deep learning research. This library is written in C++ language. This offers a platform for developers wanting to upgrade to advanced analytics.
This is one of the top artificial intelligence tools currently at the disposal of a machine learning engineer. It automates the processes involved in articulating a real world problem using machine learning techniques. This helps a data scientist to shift his focus from mundane repetitive tasks like modelling to handling problems at hand. The tool has cleared the way for machine learning for everybody, as someone without much ML experience can easily navigate in the field.
This business oriented artificial intelligence tool is an open source deep learning platform. It helps draw insightful decisions on business from the data at disposal. H2O is written in Java and includes interfaces for Python, R, Java, Scala, CoffeeScript and JSON. The tool finds application in predictive modelling, risk analysis, healthcare, and insurance analytics.
10. Scikit Learn
This is one of the most widely used libraries in machine learning community. There are certain factors that make it the go-to library for developers, such as cross-validation, feature extraction, supervised learning algorithm etc. However, it runs on single processor CPU. This library is built on SciPy, that includes Numpy, Matlotlib, Pandas, Sympy, IPython and SciPy. Its focussed on modelling the data, rather than manipulating it.
With this we have discussed some of the most used tools in artificial intelligence in the recent years. There are other AI tools that are growing in popularity like Google ML kit, Theano, Swift AI, Deeplearning4j etc. These artificial intelligence tools and techniques can further the advancement in the field and has the potential to optimize human effort in every possible area of application.