According to MarketsandMarkets report, the deep learning market is anticipated to grow at a CAGR of 65.3% between 2016 to 2022, reaching a value of $1,772.9 million by 2022. 2017 was the year where we saw great advancements in the field of machine learning and deep learning, 2018 is all set to see many more advanced use cases, with TensorFlow becoming the beloved machine learning software library for big giants like Twitter, AirBnB, eBay, NVidia, Google, Dropbox, SAP, QualComm, Facebook, Instagram, Uber, DeepMind, Lenovo and many others on the verge of adopting TensorFlow.
From hunting for new planets to preventing blindness by helping doctors screen for diabetic retinopathy, there are several real-life use cases going towards mainstream with the use of TensorFlow. 2018 Stack Overflow Developer Survey revealed that TensorFlow is the fan-favorite of machine learning frameworks with 73.5% respondents praising it.
So, since you’re still reading this post, looks like you want to start your deep learning journey or have been playing with neural networks since quite some time. Whichever case it be, you are in a bit of a dilemma as to what makes TensorFlow so special compared to other deep learning frameworks and libraries. Fret not! We are here to help you make it easier and quicker for you to understand on why you should choose TensorFlow for Deep Learning. This article is for data enthusiasts or professionals interested in learning more about what makes TensorFlow internet’s most favorite open source machine learning project. This post will answer questions like – “What is TensorFlow?”, “What is TensorFlow used for?”, “What are the applications of TensorFlow”? and what makes TensorFlow the most popular open source machine learning project.
TensorFlow was developed by engineers and researchers working on the Google Brain Team within Google’s Machine Intelligence research organization. Earlier known as DistBelief , it was built in 2011 as proprietary system dependent on deep learning neural networks. The code of DistBelief was altered in 2017 to develop a better software application library known as TensorFlow since 2015. The main objective of making TensorFlow open source was to ensure that all new research ideas are implemented in TensorFlow which will help Google productize on those ideas first.
Since 2015, TensorFlow has gained huge importance within the data science community and ranks #1 among the popular deep learning libraries for Data Science. In 2017, Google released TensorFlow Lite that aims at helping developers build machine learning solutions directly for embedded IoT and mobile devices. TensorFlow Lite provides superfast performance on small devices and works well with all Android and iOS devices.
With more than 1500 project mentions on GitHub and over 6000 open source repositories showing its roots in various real-world research and applications -TensorFlow is definitely one of the best deep learning library out there. The constant growth and consistent updates add a feather to its cap of popularity making it the fan-favourite machine learning framework amongst researchers and developers.
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“TensorFlow – The lingua franca of machine learning researchers and developers.”
TensorFlow is an open source customizable software library for performing numerical and graphical computations using data flow graphs. A flexible, scalable, and portable system used for creating large-scale neural networks with multiple layers. The base language for TensorFlow is Python or C++.TensorFlow provides fantastic architectural support that make it easy to deploy complex numerical computations across diverse platforms ranging from PC’s to mobiles, edge devices, and also cluster of servers. TensorFlow has been designed for use both in research and development and in production systems. TensorFlow might be an overkill for simpler tasks but a strong bet for complex deep learning tasks.
"TensorFlow doesn't solve the problem, but gives you the toolkit to abstract away from academics of a convolutional neural net and use one to solve your problem.” Dan Nelson, head of data at Ocado Technology told Computerworld UK
People often make a case that TensorFlow’s popularity as a deep learning framework is based on its legacy as it enjoys the reputation of the household name “Google”. TensorFlow, no doubt, is better in terms of marketing but that’s not the only reason that make it the fan-favourite of researchers.
TensorFlow provides highly flexible and modular architecture meaning you can use only the required parts or use all the parts together. TensorFlow integrates with anything than can call a simple C API and also deals with limited concepts such as a sessions, Tensors and a DAG. Computations need to be expressed as a data flow graph and TensorFlow provides multiple versions of the same model or multiple models for execution. Developers can split the design of the data flow from its execution. Build up the data flow graph and then send it for execution on the CPUs of machines or to the GPU’s or a combination of the two. All this takes places through a single interface hiding all the complexities from the user. Because the execution is asynchronous it scales across multiple machines and can tackle large volumes of data. This facilitates non-automatic migration to new models/versions and A/B testing of experimental models.
If you need high-performance models that can further be optimized and speed is of utmost importance for the model then TensorFlow is the go-to framework of choice. With support for threads, queues, and asynchronous computations, TensorFlow lets you make the most of your available hardware. Moreover, the cloud TPU hardware is meant for working with TensorFlow providing unmatched speeds. Instead of churning data on older CPU’s , cloud TPU’s can be used for superfast results.
Developers always want to enjoy the experience of working with a software library and TensorFlow has been built with that mindset. The highest level application program interfaces are tuned for ease of usage and learning. Just with a little experience, developers can get a knick-knack on how to handle the tool and understand what kind of changes will result in the change of complete functionality. The lowest level API i.e. the TensorFlow core API provides fine levels of control to work around with the model. All other higher level API’s are built on top of the TensorFlow core API making it easier to perform repetitive tasks.
Organizations are often burdened with portability and TensorFlow overcomes this challenge by allowing developers to play around a novel idea on their laptop without requiring any additional hardware support. With TensorFlow developers can deploy a trained on a mobile and this how it provides true portability.
It is easy to focus on features, capabilities, and benchmarks of a machine learning model but difficult to write a code that humans can use vs. code that machines can compile and run. The best thing about TensorFlow is that everyone in the machine learning community is aware of it and are open to trying out so that others can use of it to deploy meaningful models. It’s like more intelligent minds solving problems, more shoulders to stand upon.
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