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1. Introduction to TensorFlow for Deep Learning
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2.  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.

3.  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.

4.  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.

Introduction to TensorFlow
 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.

5.  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.

What is TensorFlow?


6.  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


7.  Why use TensorFlow for Deep Learning?
 TensorFlow supports both CPU’s and GPU’s computing devices for distributed
computing.

It has faster compilation time compared to other deep learning libraries like
Keras and Torch.

It is easier to work with TensorFlow as it provide both C++ and Python API’s.
One can experiment in a rich, high-level environment and deploy models in
environment that requires native code or low latency. It now runs in many
other programming languages, from R to Swift to JavaScript.

TensorFlow has a much bigger community compared to other deep learning
libraries meaning it is easier to find several resources and MOOC’s to learn
TensorFlow.


8.  TensorFlow has readable and accessible syntax which is important for ease of
use. Considering the advanced nature of machine learning, complex syntax is
the last thing researchers and developers would want to work with.
 Provides high performance implementations for various learning models like
LSTM RNN and Stochastic Forests.

Has TensorBoard for excellent data visualizations.

What makes TensorFlow popular ?
 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.



9.  i) Architectural Flexibility

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
takes places through a single interface hiding all the complexities from
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.


10.  ii) Fantastic Performance

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
make the most of your available hardware. Moreover, the cloud TPU hardware is
for working with TensorFlow providing unmatched speeds. Instead of churning data on
older CPU’s , cloud TPU’s can be used for superfast results.

iii) Easier Control through Multiple API’s

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
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.


11.  iv) Portability
 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.

v) Excellent Community Engagement

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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