Artificial intelligence takes the highway to technological advancements as Google makes TensorFlow open source. TensorFlow was used by Google in many of the features and Google products. Some examples are Google Photos which return results to you by recognizing a place or objects in a photograph. Google’s voice recognition app and Google translate are other products that use TensorFlow.
How does it Work?
TensorFlow is an advanced version of DistBelief which was created by Google in 2011. DisBelief used machine learning to identify pictures by looking for certain characteristics in the photo. TensorFlow does the same thing but it moves step by step from one node to another gathering enough information for the identification of the photo.
Open sourcing such technology can make it easy for developers to come up with innovative ways of using the TensorFlow. In addition to this, Google explains that the technology is at its prime and needs to be developed further. By making TensorFlow open source, Google intends to enhance the use of this machine learning toolbox by making it available for anyone who can put it to use.
TensorFlow can be used by anyone who can express their computation as a data flow graph. Google provides many helpful subgraphs which are quite common in neural networks, but if you choose to put together your own subgraphs then you can do that as well. The flexibility of TensorFlow makes it easy for you to innovate around the tool so that you can come up with a satisfactory outcome.
TensorFlow can be used on your laptop without the need of special hardware, you can also run it on GPUs, desktops and servers. The ability of TensorFlow to work on different platforms without any change in codes makes it easy for you to play around with it especially when you want to run it on different devices.
Smooth Transition from Research to Production
Earlier it was a great step to move from the research phase to production, but Google simplifies the entire process. With TensorFlow it is easy to transition an idea into a product and bring it to the users. Researchers and developers find this to be easier and smoother. By connecting research and production, Google adds more value to TensorFlow.
The auto differentiation capabilities of TensorFlow are great for gradient based machine learning algorithms. You can define the computational architecture of your model and then combine it with your objective function and finally add data to it. Once this is done, TensorFlow will automatically compute the derivatives for you. You can check what is happening as your graph extends when the derivatives are being computed.
TensorFlow is mainly C++ with a significant amount of Python. Google plans to bring more languages to the TensorFlow later. For now, you can write in Python and C++.
TensorFlow makes the mode of the hardware you are using. This means that if you have a great system then TensorFlow is going to optimize the use of your system.
Google has made TensorFlow open source giving everyone an opportunity to play with this machine learning tool. Google has provided this tool to the public so that innovative minds can explore the uses of TensorFlow and come up with more ideas for it. The company plans to work on TensorFlow to make it better. By opening it to the public, Google wants to create an open source community where it can receive feedback on TensorFlow and also have contributions from other researchers in order to make TensorFlow better and more efficient.
TensorFlow is easy to use and it is well-tested because Google has already used it in many of its products. The results have been satisfactory which means TensorFlow can produce positive outcome for those who plan to use it. The many features of TensorFlow make it great for developers. The fact that Artificial Intelligence is an area that needs to be explored further, developers and researchers have the chance to utilize this opportunity that is being provided by Google. TensorFlow can make development of AI technology much smoother. This means that we may see tools and apps are coming up which utilize the unique TensorFlow.