Power of Tensor Flow – How is it better than others9 min read

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Power of Tensor Flow – How is it better than others

 
 

Google’s 2015-built open-source dataflow software programming library TensorFlow isn’t new but surely better than others. Tensor flow is mathematical library and is very popular for its usage in the development of recent generation of machine learning applications, for example neural, network and Artificial Intelligence etc. It is said to be easy to access and use, due to its open-source nature, unlike its predecessor DistBelief, which was a closed-source project.

Interested in knowing more about it and understand what makes it good for you?

Here’s everything you are searching for:

 
 

TensorFlow in Brief:

TensorFlow was launched with high degree of computational graphic visualization capabilities and Natural Language Processing powers. A deep neural network modelling algorithm enhances its graphic recognition powers.

 

 

What makes this ‘second-generation ML product of Google’ better is its flexible, portable, extensible and market-ready features, which are better than DistBelief. Google has tried to overcome all the issues of DistBelief in TensorFlow.

 
 

Features of TensorFlow, which makes it Better

  • In-built support for Machine Learning

It supports deep learning at extensive level and allows you to perform any computation, which could be presented in the form of computation flow graph.

  • Efficient for Gradient-based Algorithms

The auto-differentiators (symbolic as well as numeric) and first-rate optimizers’ suite of TensorFlow makes it easy to work on gradient-based algorithms in this ML tool.

  • Industry-Ready

Apart from research, TensorFlow is rejoicing for industry users too. It has great production-ready features. The fast and portable characteristics of this library allow the users to move their ideas to mobile platforms seamlessly, once the desktop GPU is trained.

  • Python

TensorFlow uses Python interface, making is easy for the programmers to code their ideas from ground level.

  • Single API for All Devices

Unlike many libraries, it is flexible enough to let you implement the same API for all the major computational devices while using multiple CPUs or GPUs.

  • Processing Unit – The Secret of Performance-efficiency of Tensor Flow

The tensor processing unit (TPU) designed by Google, is specifically tailored for deep machine learning. This TPU is compatible for multiple CPUs and GPUs (with optional extensions like CUDA and SYCL).

  • Compatibility Equations

Light-weighted Tensor Flow is also available for mobile and embedded devices. So, it is compatible with mobile computing platforms like Android and iOS as well as traditional mac operating system, Windows, Linux.

  • Language support & Operating System Support

Tensorflow runs faster as it is structured with the great feature enriched language, Python API and interfaced with Numpy and C/C++ engine. It also supports Java API with good graphical processing unit (GPU) acceleration. Supported API languages are:

  • Python

  • GO

  • Java

  • C++

The bindings are possible for Haskell, C#, Ruby, Julia, Scala, Rust and Ruby.

 
 

How it Works?

This library is efficient to perform simple to complex problems expressed as dataflow graphs, in which, nodes represent ‘short FOR operations’ (ops) and tensor is a multidimensional array. An op takes 0 or more tensors to compute, like addition (of digits and matrices), MatMul (multiplication of two matrices) and other 3D modeling in graphical form.

 
 

Main Applications of Tensor Flow

Nowadays, it is frequently used by all the industrial niches. Some applications of this library are:

  • Google Neural Machine Translation:

The recently-developed attention wrapper (decoder) allows the better deployment of Neural Machine Translation algorithms through the API.

  • Deep Dream:

This automated image captioning software uses Tensorflow library for mapping the text to images.

  • Object Detection

TensorFlow Object Detection API is able to detect the objects once trained.

  • Applications of Sequence-to-Sequence Models

These models, build over TensorFlow are able to perform multiple machine learning operations, such as speech recognition, text summarization, NMT (Neural Machine Translation), and other such tasks.

 
 

Competitive Analysis – Tenser Flow vs. Other Libraries

  • Theano

Theono is similar to tensorflow in various features. But, it has dramatically low graphical feature recognition when compared to TensorFlow.

  • Torch

Torch is extended to use on Android and iOS and is preferred to build hardware implementations for neural networks. Torch is frequently used by Facebook AI Research Group, IBM, Yandex and so many other popular companies. However, the graphic recognition power of Tensor Flow beats Torch.

  • Caffe2

Caffe is widely used in academic research projects, industrial applications and multimedia. For industrial usage, Tensor Flow is preferable. Real-time, text and sound data formats are not perfectly handled by Caffe2.

 

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