Tensorflow- Smarter Machine Learning, For Everyone

TensorFlow – Faster/Smarter/Flexible

TensorFlow, a faster, smarter and a much more flexible than the earlier system can be adapted easily to latest products and research. Moreover it is also highly accessible machine learning system which can run on an individual smartphone or through thousands of computers in data-centres.

Machine learning is yet in its initial stage and computers presently are unable to do what a 4 year old could do naturally such as knowing the name of an animal on seeing only a few examples. However with Tensorflow, it is a good start.

TensorFlow is utilised for all purpose, from speech recognition in Google app to Smart Reply in Inbox, to search in Google Photos. It has the potentials to build and train neural nets up to five times quicker than the first generation system and hence it could be used in improving the products in a quicker manner.

Deep Learning has had a great influence on computer science, with the possibility of exploring new areas of research and in the creation of incredibly useful products which millions of people tend to use daily. The internal deep learning infrastructure – DistBelief developed in 2011, enabled Googlers to create even bigger neural networks and scale training to thousands of cores in data centres.

Twice as Fast as DistBelief

In 2014, DistBelief has trained the Inception mode which had won Imagenet’s Large Scale Visual Recognition Challenge, driving the experiments in automated image captioning and DeepDream. DistBelief had some limitation though it was very successful and was closely directed to neural networks.

To organise it, was difficult and it was firmly coupled to Google’s internal infrastructure thus making it difficult in sharing research code externally. The open source release of TensorFlow has been publicised, the second-generation machine learning system which has been designed in correcting shortcomings. TensorFlow is flexible, portable, general, easy to use and is a completely open source. On enhancing DistBelief, addition of speed, scalability together with production readiness has been done.

 TechFlow is twice as fast as DistBelief.TensorFlow tends to have all-encompassing built-in support for the purpose of deep learning though it is more general than any computation. One can express as computational glow graph and can compute with TensorFlow.

Any grade based machine learning procedure could profit from TensorFlow’s auto-differentiation and set of first rate optimizers. It tends to get easy in expressing new ideas in TensorFlow through the flexible Python interface.

Open-Sourced TensorFlow – Standalone Library

One can move their idea flawlessly from training the desktop GPU to running on the mobile phone. Moreover you could also get started rapidly with the powerful machine learning tech by utilising the state-of-the-art example model architectures.

However, the most important fact about TensorFlow is that it your own possession. It is an open-sourced TensorFlow as a standalone library associated with tools, tutorials and examples with the Apache 2.0 licence. Hence, the user is free to use TensorFlow at their institution, at any place of work.

TensorFlow has been utilised for all the experiments by deep learning researchers and the engineers tend to use it to permeate Google Search with signals from deep neural networks as well as to power the features of tomorrow. TensorFlow will continue to be used in serving machine learning in products and the research team has been committed in sharing its implementation of available ideas.