Greetings! I'm Aneesh Sreedharan, CEO of 2Hats Logic Solutions. At 2Hats Logic Solutions, we are dedicated to providing technical expertise and resolving your concerns in the world of technology. Our blog page serves as a resource where we share insights and experiences, offering valuable perspectives on your queries.
Computing technologies are evolving more than ever into the AI (Artificial Intelligence) space. Big tech companies already started to show off their AI-powered applications. An example would be the Google assistant that can fix a haircut appointment. Companies like Google have been inviting developers to the development process, but this required big data sets and better machines.
In 2015 Google released Tensorflow, an open-source Machine Learning library with the data flow of machine learning and neural networks for research and production. Machine Learning (ML) is a prominent approach to AI implementation. Tensorflow provides APIs which developers can easily use to create apps with the power of AI.
Advantages of Tensorflow
Tensorflow has a rich set of data for application programming interfaces. It helps deep learning project and applications in most major languages. Tensorflow’s wide library includes sentiment analysis, object detection in photos, and cancer detection. Numerical computing is critical for deep learning in which tensorflow excels.
Even if Tensorflow is in its development stage, it’s getting better and better every day. The team already released a large number of data models already and we can find that easily in https://github.com/tensorflow/models. We can use any of the major languages such as C, C++, Python, Java, Android, IoS, Mac OS, Windows, Linux and Go.
Why will Tensorflow change the way apps are built?
It became a necessity for most of the applications to include AI in it, which power up the app and increases the number of users that can interact without human intervention. This requires high machine learning abilities in most cases. This requires powerful computers and small-scale applications cannot afford such a computing capacity. That’s where Tensorflow comes to play with its super processing speed and response time.
It doesn’t just change the way apps are built but changes are high that apps are built around Tensorflow making use of its abilities.
Some examples of apps that can use Tensorflow would be:
-Application for auto email responses
-Fitness apps that can scan a posture and recommend workout patterns
-Apps that can detect cancer symptoms
-Handwriting recognition
Well, the list can go on. Imagine all the time and resources Tensorflow saves so better Machine Learning AI applications can be built with less effort. It definitely is going to help the app builders and the way they are going to be built.
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