Machine learning is revolutionizing technology. This technology has successfully been able to tackle a number of tasks which were only carried out by humans until now. From translating speech to driving cars, machine learning is driving an explosion of opportunities into the world which can help all of us to make some sense of the unpredictable and messy real world. However, when a technology is this helpful then obviously raises a question of what exactly is machine learning? According to experts, machine learning can be defined as the process through which a computer system can be taught to make accurate predictions which the appropriate data is fed to it. Those predictions can further be used to answer a number of potential questions like whether the image is of a banana or an apple, whether an email is spam, or to recognize speech. And if you are on a quest to learn more about this technology then it is vital for you to know about the 4 machine learning technologies. And those 4 important machine learning technologies are mentioned below.
- Apache MXNet
Apache MXNet is currently an incubator project in the Apache Software Foundation. It can be understood as a type of open source deep learning framework. However, what makes this open source deep learning framework so special is that it has been selected by the Amazon Web Services to be a deep learning engine of their own choice! This clearly shows that this framework has a lot of potentials and a business giant like Amazon is working on the framework to evolve it and make it a better version of itself.
Pytorch technology is actually a source machine learning library. This library has specifically been designed for Python and was based on the Torch machine learning library. The origin of this technology can be traced back to the AI research group on Facebook. This library can also be defined as a deep learning framework which has specifically been designed to allow flexible and quicker experimentation. This technology comes as a Python package. It also includes a tensor computation with a deep neural network and strong GPU acceleration.
Keras is a technology which is considered to be a high-level API which was originally built on the top of TensorFlow. We will be learning more about TensorFlow in the next section. However, at this point, it is important to note that Keras is widely considered to be more user-friendly when it comes to accessing the advantages of TensorFlow without feeling the need to go deep into the TensorFlow itself. But it is important to note that you will also be missing out on many benefits of TensorFlow including its debugging capabilities. Depending on the application, Keras can still be a good choice. It is also quite interesting to note that Keras was initially developed as an integral part of the research efforts of the project ONEIROS.
TensorFlow is the technology which comes last on our list. But this doesn’t mean that this technology is in any way of the least quality when compared to the other technologies on the list. Instead, many experts actually rate this as the best machine learning technology which one should definitely know about. The history of the development of TensorFlow starts with Google. The predecessor of TensorFlow was developed by Google and it was intended to be a proprietary machine learning library used primarily for deep neural networks. This predecessor was used for years by Google and they eventually released a simpler version of this technology for open source in 2015. Because of that, TensorFlow now has its own ecosystem which includes some related technologies, an active community of users, and a blog. If you wish to learn more about TensorFlow and how to use it then, there are many tutorials online which you can access on its official website.