Deep Learning is changing the Technology world , In fact Technology people are getting crazy about it these days . If you look at the biggest IT firm like Facebook , Google , Microsoft all are targeting to achieve excellence in Deep Learning . Even the magic behind Google Brain is Deep Learning . If you are software professional or technology lover please note it ” Machine learning is eating software industry and Deep learning is eating Machine Learning ” . Before we go proceed , I think we should explore Google trend on Deep learning .This will enforce you to invest your five minutes in this article . This article covers a seed ground to Deep Learning Framework : TensorFlow .
Before we explore any Deep learning framework .If you need to brush up your basics in machine Learning go for the article What is machine learning ? Apart from it , you should also explore the difference between Deep Learning an Machine Learning for better understanding of deep learning framework .
What is TensorFlow –
An open source Deep learning frame work which is distributive in nature . You can run Tensor Flow on multiple platforms like Mac , Windows and Linux . TensorFlow support multiple GPU/CPU architecture . This architecture can distribute the training of neural network into various server or node . Hence it support high performance AI based products.
I know you are still searching for the answer why TensorFlow is considered among other deep learning framework . Be patience read the complete article , It will give you amazing facts towards TensorFlow .
Reason to choose TensorFlow as Deep Learning Framework-
1.Cloud services for TensorFlow-
Tensorflow is powered by Google Deep Learning Cloud .See Google cloud services are not limited to TensorFlow only but It has specific support for Deep leaning framework TensorFlow .
2. TensorBoard –
It comes as a default part of TensorFlow . It is a Data visualization tool which gives us the features of drawing learning curves and computation graph . As you know that computation and training related to deep neural network becomes complex and massive . In this situation using a data visualization tool make easier to traverse and explore the code .
Machine Learning Gradient descent –
TensorFlow is such a powerful deep learning framework which provide the feature of Automatic differentiating . I think , You have not understood it Right !
See every machine learning algorithm comes with an optimization algorithms inbuilt with it . Hence gradient descent is a kind of machine learning optimization algorithms which can easily club with other . TensorFlow auto take care of gradient of function which minimize the cost of function.
High performance API –
TensorFlow is combination of multiple High performance API . Here I will mention only Top two , just It is an introductory article .First is TF-Slim which is a helping hand in trimming the data set . Second is TFlearn which provide Higher level API support to TensorFlow . This TFlearn helps in understanding and using of deep neural network easy .
Magnificent Architecture –
TensorFlow has a magnificent architecture . Actually it has an independent core ,named TensorFlow core which provides hard control over model . That is the reason why machine learning researchers user TensorFlow core . Apart from it , TensorFlow support high level of API like tf.estimator which helps in managing data set . These high level API automates various repeated task , This make developer life easier .
C++ in TensorFlow core –
You must have listened that TensorFlow is leader in the term of speed . Even , In this article we have discussed it earlier . Are you excited to know about the reason ?
Well the core of TensorFlow use C++ . This make it fast . Even you can also implement your own ML operation in C++ and Integrate that in TensorFlow .
Python as a development language –
I think every one of us is Familiar of python . Anyways I will not go in depth for python in this article , In the place of that I will recommend you to have a quick view on the article Python for Data Analysis a Complete overview . This article will help you in understanding the use of python here . Apart from it , If you need to quickly brush up your Python skill , Please read the article Learn Python Essential in 5 minutes . This articles will make you remember of Python Syntax quickly when you read these two article , You will be fan of Python Programming Language .
Power of Hadoop with TensorFlow –
Running TensorFlow on Hadoop is itself an adventure . Of course , In a single introductory article we can not discuss every thing . So I will give a informative link which best explains how to use TensorFlow on Hadoop framework . You can also learn setting up multiple cluster of server for TensorFlow here .
Community Support for TensorFlow –
The people behind TensorFlow is the team who developed Google Brain . In my view , Nothing more is required for discussion on founder team of TensorFlow .It is an open source and you can reach out TensorFlow on Git . You can also reach out many solution over Stackoverflow. So many data scientist use Quora for discussion . Feel free to ask your doubt in this type of community .You will get the solution soon .
End Notes-
I hope you must have enjoyed this article. The aim of this article is to give a overview on TensorFlow . TensorFlow is not only a deep learning framework but it is also useful in complex mathematics computation . I have heard physics scholar also use TensorFlow a lot . It’s distributive architecture makes it scalable , High performing and customized Deep learning Platform .
In our next article ,” How to setup your machine for TensorFlow” , We will assist you in making your machine ready with TensorFlow .I promise ,If you follow this complete blog series ( I mean next few articles ), You will have strong hands on TensorFlow in very less time . Anyways ,Do write your feed back about this article . Your comments and feed back help us to identify the gap between our article and your learning requirements . Once we understand the gap , We will fill that asap .
Data Science Learner Team
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