I think everyone would agree – our brain is one smart supercomputer. A task that is as simple as crossing the street is so automatic for us humans because our brain acts like a super-intelligent autopilot. When you cross the street, your brain automatically looks for oncoming vehicles. Judging by the car’s velocity and acceleration, your brain could either tell you to go or wait so that you won’t get run over.
The human brain is amazing, which is why computer scientists and programmers have started modelling computers after our very own human anatomy. The area of artificial intelligence, artificial neural networks, machine learning, and predictive analytics has become exponentially popular in the recent years.
As humans, we learn by experience. What happens to us every day is collectively stored in our memory, and we learn from that. The reason why you would not touch a hot stove is because you probably touched it before. The choices and decisions we make every day is mostly a product of our learnings taken from our experiences.
But computers are non-sentient beings – they do not have emotions or feelings. Experience is not something that is understood by computers. The question is this: how do we make computers learn? We make them learn through data.
Data is nothing more than a complicated string of binaries – ones and zeroes. Computers can understand data and by correlating how these data inputs affect the outputs, computers learn. This process is called machine learning.
Machine learning, just like a human nervous system, needs a framework that defines its functionality and overall system. This framework is an interface that allows programmers to build computational models. Many artificial intelligence business software like WorkFusion’s Smart Process Automation are developed based on various machine learning frameworks, operating systems, testing platforms, and programming languages.
Here are three machine learning frameworks that are commonly being used:
Considered as one of the simplest machine learning frameworks to set up and use, Torch is used by many big tech companies like Twitter and Facebook. If you are using Ubuntu, Torch is a great machine learning framework to try.
Amazon’s use of machine learning is very evident to any Amazon website visitor. This is built on their own machine learning framework called AML. You can use AML with very little coding and instead, develop your learning models with its collection of tools and wizards.
The name Apache is widespread in the cyber world. It comes from Apache Software Foundation, which is renowned in the world of computing and programming. It is for the implementation of scalable machine learning algorithms that specialize in clustering, classification, and data filtering.
Still, from Apache Software Foundation, Singa provides a simple, robust deep learning model that is focused primarily on natural language processing and image analytics. Its software stack consists of three major components: core, IO, and model.
CNTK, or Cognitive Toolkit, harnesses intelligence from massive datasets for text recognition, image analysis, and speech recognition. This is Microsoft’s open-source machine learning framework. Many people think that CNTK is one of the easy to use machine learning frameworks right now.
Developed by the people behind the Google Brain, Tensorflow is an open source machine learning framework used for extensive research on machine learning and deep neural networks. It is actually the second machine learning framework by Google Brain and was initially developed for internal Google use. Many Google services are equipped with Tensorflow including Google Search, Gmail, Google Photos, and Speech Recognition.