The Information Technology has many reasons to throne Google as the righteous pioneer. No wonder that the California based firm is steering the industry voluming a bulk of technologies that are at infancy though, but capable to run the future far better than any competitor. TensorFlow, Google’s Machine Learning Tool, is one such from the volumes that recently stamped in the opensource fragment of technology. Created from scratch, TensorFlow ramps all the corners from speech recognition in the Google app, through Smart Reply in Inbox, and upto search in Google Photos.
Before advancing onto TensorFlow, I possess something to deploy to your cognizance and that’s one of the scanty-frailty stories of the California based internet giant, though they don’t wholeheartedly accept the same. In 2011, Google enumurated the stature of machine learning and unleashed a deep learning infrastructure, DistBelief. Notwithstanding search engine giant’s years of exposure, DistBelief was more a broader symmetry of machine learning making Googlers to build ever larger neural networks, but limited narrowly to neural networks. Google also hardly found ways out to configure the technology, and to disclose DistBelief’s research code as it’s resolutely coupled to Google’s internal infrastructure. So a newborn arose in the hub – TensorFlow.
What is TensorFlow?
TensorFlow is Google’s second-generation machine learning system, the successor of DistBelief, which is used for numerical computation using data flow graph. The system which is still at infancy is open source, flexible, general, portable, and easy-to-use.
Now, have a look at the gif image below.
The image is a data flow graph describing mathematical computation with a directed graph of nodes & edges that represents the work flow. While nodes stands for mathematical operations, the edges do it for the multidimensional data arrays, which are tensors.
Why named TensorFlow?
The above statement somewhat gets you the idea behind the name, TensorFlow. Nodes customarily employ mathematical operations, represent endpoints to feed data in and out, may be in the form of results, and works on reading or writing continual variables. Meanwhile, edges depict the input or output relationships between nodes. During the action, dynamically-sized multidimensional data arrays or tensors are carried by data edges. Hence the flow of tensors happens in the development, it gets it name nothing other than ‘TensorFlow’.
Why TensorFlow is special?
It’s neither fixed nor you’re an audience! TensorFlow is a flexible deployment system for deep learning neural networks. You build innovations if you can pull out computations as a data flow graph. Since TensorFlow is much flexible one could write the inner loop and construct the graph to drive computation. To make it even more flexible, the search engine giant enables helpful tools to construct subgraphs common in neural networks. It also allows developers to write their own higher-level libraries on top of TensorFlow.
Doesn’t matter the platform, you can run TensorFlow on CPUs or GPUs, and on desktop, server, or mobile computing platforms. That is, you can take your laptop to work on your machine learning idea, use the same idea on GPUs with no code changes, install the same model on any mobile, or you can even run the same idea a service in cloud. TensorFlow is highly portable.
Research links production
No need to go after a major rewrite with TensorFlow as it helps you to connect your research with production. More specifically, with TensorFlow industrial researchers can easily turn ideas into products faster.
The automatic differentiation capability is one such an important feature of TensorFlow as it helps charge gradient based machine learning algorithms. More precisely, TensorFlow takes care your computing derivatives. All you have to build is the computational architecture of your predictive model, and combine that with your objective function along with data.
By default, Python and C++ are the interfaces used in TensorFlow, which are said to be easy-to-use languages by Google developers to build one’s computational graph. Google says that TensorFlow is at its infancy phase and hopefully in the coming days developers will contribute
Frame the most from the available hardware. Got a machine with 32 CPU cores and 4 GPU cards? TensorFlow is well embellished to fork everything to get the utmost performance in reality.
Why did Google opensource TensorFlow?
The official statement according to Google is that machine learning has left a lot for the future in technology and innovation. So a huge amount of research and efforts are needed in action to grow fast by chopping off the current issues. Keeping TensorFlow as Google’s own property won’t result in an enormous makeover, but opensourcing the same will create new potential for machine learning when people exchange their ideas and thoughts experimenting in new-new products, which will eventually impact a great evolution. Online advertising and computer security are a couple of the many beneficials for the rest of the world.
Nevertheless, there is a hidden truth behind Google’s opensourcing strategy. And why didn’t Google opensource DistBelief, if world’s future in machine learning and innovative products were their prime focus? Indisputably, it’s Google’s business strategy in a highly competitive environment as many biggies and startups including Microsoft, Intel, Apple, Samsung are running researches on the same core, moving into more public desirability is no short of a mighty deal. Moreover, Google still needs to find perfection in its image search, internet search, spoken word recognition, translation; even though it’s one of the most impressive and accurate search engines. So, Google believes that it can accelerate the evolution of AI getting returns of its initiative.
Checkout the video and see what the Fathers of TensorFlow vocalize.
Things to Consider Before Hiring the Right Developer
As the tech talent war continues to rage, it is no wonder that hiring the right developer takes forever. Managers face numerous difficulties not just hiring but retaining the right talent.If we are to go by statistics, the average time-to-hire for tech positions in the USA is 35 days. A realistic expectation is crucial while […]
4 Ways To Reduce your App Development Costs by 50%
Being multi-channel has become synonyms with the modern digital environment.Brand presence in these channels is critical to reach potential customers making it a vital business challenge of the digital era. Mobile apps have become an integral part of modern business. They are an extension of your brand. Considered as the most powerful business tool, apps […]
Tips to build a productive startup culture in your development company
The word Startup paints a picture of tech-savvy professionals driving casual dress codes and sipping coffee from biodegradable cups in a space with enviable interior design, ping pong tables and bean bags. Ouch, the stereotype hurts! There is more to startups than bean bags and beer taps, it is the startup culture that fuels innovation […]