Artificial intelligence (AI) and machine learning are the new well-sought after career areas in development and IT organizations. In fact, businesses are competing to get the best talent in these fields due to the current shortage of skilled and qualified experts in the market.
To fill this existing gap, many technology experts are seeking to bolster their skills with technologies that are vital for both AI and machine learning, for instance, through learning various computer languages like Python among many others.
Despite all that, the only concern is about the technology beyond the languages such as machine learning libraries, for example, which ones you ought to watch and which ones are vital to know. This case has no simple answer since there are numerous libraries and frameworks today. Worst case scenario, most are ever-evolving while new ones are being created.
In spite of the existence of many libraries and frameworks, they are expected to reduce to a handful. The reason is that some of the four leading players in the industry including Microsoft, Google, Facebook, and Amazon are currently trying to build libraries and software. Even so, current times are considered tricky since a model developed using a single library cannot be easily utilized by a model that was written using another library.
Below are 5 non-language machine learning (ML) technologies that you ought to know.
This Python library allows you to define, enhance and assess mathematical expressions that involve multi-dimensional arrays. The open source project was created by the University of Montreal’s machine learning group.
This Python open-source machine learning library is based on the Torch machine learning library. It traces its origin from Facebook’s AI research group. It appears as a Python package that is made up of tensor computation coupled with deep neural networks and strong GPU acceleration.
This high-level API developed on top of TensorFlow is considered to be a more user-friendly method of accessing the benefits associated with TensorFlow without having to go any deep into TensorFlow itself. For this reason, you may miss out on various benefits that come with TensorFlow like its debugging features. Nevertheless, this ML technology is still an ideal choice, depending on the preferred application.
It was the most mentioned vital machine learning technology by experts in the field. Google first created TensorFlow’s predecessor as a proprietary machine learning library intended for deep neural networks. In fact, the company used it in all its companies for years before launching a simplified version in 2015 to open source. Currently, TensorFlow boasts an active community of user groups, a blog and its own ecosystem of associated technologies.
This open source deep learning framework currently works in the Apache Software Foundation as an incubator project. It was recently selected by AWS as its deep learning tool of choice. Furthermore, Amazon has devoted a huge team to collaborate with the MXNet community in improving the framework.