Last year November Google opened up TensorFlow, this is a software product, basically, a machine learning software which powers its services such as Google translate and Photos. The tech giant has made yet another great step for the software by giving TensorFlow a learning equivalent of smart pills. For those who don’t know, TensorFlow, in reality, is nothing other than an open-source library (a software) and it is used for numerical computation and it works by utilizing data flow graphs. It was developed by engineers working on the Google Brain Team within Google’s Machine Intelligence for the purpose of conducting machine learning. The company has now done a very big upgrade for TensorFlow.
How to make it smarter?
According to new tech news, a way you can make machine learning software a lot smarter is by providing it the ability to analyze a lot of data which ultimately includes examples such as searching for common trends like facial features found in photos and of course properties. By allowing TensorFlow to run these kinds of operations on computer networks simultaneously rather than on individual machines gives users the ability to make smarter systems and improve them faster. It can now even be positioned on cloud platforms and trough out dozens and dozens of distributed machines. This really provides the framework a lot larger room to learn more.
Scaling across distributed machines
With the ability to scale across distributed machines TensorFlow models can now be trained and deployed on Google Cloud Platform thus complementing the Google Cloud Machine Learning which is a completely novelty and is offered to firms and developers who wish to create Google cloud-based machine learning software as well as apps. Giving TensorFlow access to the combined power of a number of computers instead of having to rely wholly on a single computer accelerates the overall data throughput of machine learning models and the speed at which accurate results are delivered. Neural networks can now learn much faster than the network that is on one computer.
A feature worth on waiting for
This was one feature that developers were eagerly waiting for and had even requested the ability of tens or hundreds of computers to work in parallel thus reducing the amount of time to taken by heavy tasks from weeks to hours. This will increase the popularity of TensorFlow considering that it is already really popular currently. The public reaction has been good so far and this has made TensorFlow very popular and the most forked project in 2015 despite being released late in the year.
Is Google using TensorFlow and how?
New tech news confirms that the company has also released new libraries of machine learning model architecture. This is basically used for training the inception neural network and it also gives all the developers an option to define their models for distributed machine learning. This update is definitely a big thing for developers who wish to upgrade further more. Google is already using TensorFlowin some of its services such as Photos and Google Translate.
Developers and their ability to build new code
Developers were given the ability to build new code, around TensorFlow when Google made the code available to open source communities in 2015. Google has taken some of the innovations and developments and integrated them with its own machine learning services. This has created an ecosystem that serves the firm as well as the open source world. Considering that machine learning can be a fairly complex aspect of computing, tapping into the wide knowledge of the open source community is a smart way of taking off some of the burden of development from a single company.
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What causes all the excitement?
TensorFlow 0.8 has been released and this is also another factor that is causing the excitement. These are the kinds of projects that stand to benefit from the distributed computing upgrade. This last project is the creation of a designer known as David Ha. He believes that TensorFlow has made it easier for the broader community to participate inapps and software deep learning. Google’s platform is consistently designed and the results are easy to share. There are some other machines learning programs out there, some of which are actually powered by TensorFlow itself.
Conclusion
Although open source programs lead to development and improved performance due to the input from different developers, it is worth remembering that the nature of open source programs leaves room for code flaws and major bugs to creep into the program unnoticed.All in all, this is major upgrade for TensorFlow and it is definitely going to take machine learning to the next level.
References
https://www.fusecrunch.com/
http://www.wearabletechdigest.com/
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