Why Use TensorFlow for Machine Learning Solution?

As a Machine Learning (ML) and deep learning framework TensorFlow is widely used by developers. It is licensed as per free and open-source library. Tensor Machine Learning, as it is often popularly abbreviated as, technology was initially developed by Google’s Brain Team, and they released the library framework in November 2015. The library was developed exclusive on the Python programming language platform. TensorFlow eases the whole process of ML development easy and fast. It is noted for its data flow fluency and ability to perform numerical computations.

TensorFlow provides multiple capabilities to create training deep neural networks to run the following:

  • Classification of handwritten digits
  • Image recognition
  • Word embedding
  • Creating recurrent neural networks
  • Video detection
  • NLP or natural language processing amongst other functions and use cases

TensorFlow can also run-on mobile devices and utilize the power of multiple GPUs and CPUs.

As you might have noticed, the library name is composed of two separate words. The first part of the name- Tensor refers to multidimensional arrays while the world “flow” is used to describe data flow during computational operations. Together the library name defines data flow in multidimensional array or Tensor arrays.

TensorFlow ML Features V/s Other ML Platforms:

  • TensorFlow is Google’s successor to Theano. It is only natural if you find the two libraries to resemble each other significantly. Some of the key team members who developed the Theano library like Ian Goodfellow were on the TensorFlow’s development team.
  • As things stand today, tensor machine learning comes without support for inline matrix operations, and you would need to copy a matrix before you are able to perform operations on it. If the matrix is significantly large the whole process becomes costly from all aspects. It would take TensorFlow almost four times more time to process such matrices than other state-of-the-art tools available for deep learning. According to Google they are trying to come up with a solution to address the issue.
  • Like most deep-learning libraries, a Python API powered by a C/C++ engine is how tensor deep learning project was coded which accounts for its significant speed. The developers of the library have provided Java API support as an experimental feature. It is important to remember that this functionality is hardly stable. So, Scala and Java developers have minimum accessibility to TensorFlow as a ML solution.
  • TensorFlow is significantly slower than other frameworks like Mx Net and CNTK
  • TensorFlow is more geared towards deep learning than anything else and accordingly provides developers with a host of support tools related to reinforcement learning and other related ML algorithms.
  • TensorFlow lacks commercial support from its developer i.e., Google. And while it is indeed a new tool for researcher, it is not at all likely that Google will provide enterprise level support for open-source projects.
  • Like its predecessor Theano, TensorFlow too works through a computational graph. A computational graph is a series of matrix operations, and the graph serves to carry out automatic differentiation. Automatic differentiation is critically important for neural network-related development. Without it, developers and researchers would need to hand code new variations of the backpropagation system whenever they made fiddled around with the neural network arrangements. Such graphs power most of the functionality for Google Brain. But those tools don’t fall under the open-source framework. In October 2017 Google introduced a dynamic computation graph module- Eager for the TensorFlow library.

The first thing that business owners need to determine while using TensorFlow is whether they want to depend on Google for the tool. The method put in use by Google while creating the Android platform and how it is conspicuously lacking in enterprise support does not encourage TensorFlow usage from the enterprise viewpoint.

What are the Top Reasons to Use TensorFlow?

Like other frameworks, implementing an end-to-end workflow solution on TensorFlow calls for the investment of a significant amount of time and effort. Many tensor machine learning features and elements are innovative. The following list of the things you can do with the library should however definitely illuminate you how the framework can be of use to you:

It is an Immensely Powerful ML Framework

Developers and researchers with huge amounts of data or those who are keen to explore the next big step in the development of Artificial Intelligence (AI) should consider TensorFlow. As a ML platform TensorFlow finds many applications. In this context, we should think of the library as the foundation of state-of-the-art projects like AlphaGo and Google Cloud Vision. You can download and make the framework weave its magic immediately or at least in the time it takes for you to learn how to use it.

TensorFlow Makes Community Engagement Easy

In the ML world it is particularly difficult for developers to create code that humans can understand and function as per intention after machine compilation. This is one of the great advantages of TensorFlow. Almost all members of ML community know the framework well while others are keen to experiment with it. The collective problem-solving that TensorFlow facilitates makes developing products using the library significantly easier.

TensorFlow is paving the way for openness, clean APIs, and helpful modules in the ML community. Further many developers are getting interested in deep learning through their interaction with TensorFlow which will eventually lead to the development of innovative reinforcement learning models. Google’s DeepMind project too adopted TensorFlow from Torch signalling TensorFlow’s growing popularity.

You Get the Freedom to Determine your Approach to ML

During the initial days of TensorFlow it used to be exceedingly difficult to learn, even by experienced and seasoned developers. The Eager module we talked about earlier changed all of that. With the addition of the module developers can interact with TensorFlow solely with Python. That means you don’t have to create voluminous graphs to work with the framework and old-school coding would work just as well for you.

TensorFlow Goes Far Beyond Python

Some developers complain how Python assumes most of the programming attention with TensorFlow. But this is not the whole truth. TensorFlow facilitates programming work in several languages as vastly different from each other R to Swift, with it you can also use JavaScript for Machine Learning.

Additionally, with TensorFlow you don’t need to start from scratch. With some familiarity with the platform, you can use the codebase of other projects and customize it as per your need.

Do Your ML Tasks Solely from your Browser

People fed up with clunky desktops than simply refuse to do the number-crunching that TensorFlow calls for will find TensorFlow Lite to be immensely helpful. With the Lite variant, developers can execute models on things as diverse as an IoT (Internet of Things) or mobile device. Inference Speedup is 3x with Lite and now you can leverage the power of ML on mobile or Raspberry Pi devices.

Create Special Builds for Smaller Devices with TensorFlow Lite

With the creation of TensorFlow.js developers can create ML models and train and execute them without ever leaving your browser. There are plenty of demos out there on the internet to help you along the way and you will soon feel right at home with the entire process.

You Can Create Neural Networks on a Line-by-Line Basis

Using TensorFlow with the Keras API lets developers create neural networks simply and efficiently. Keras works especially well if you are looking to develop simple prototypes or need a significant amount of user-friendliness, something that TensorFlow lacks in severely. With the development of tf. keras developers can now build neural networks on a layer basis and using an object-oriented approach.

New, Enhanced Data Pipelines

With the addition of the tf.data namespace tool, developers who found TensorFlow to be too difficult to do something useful now have an easy solution. The namespace lets developers to carry out input processing with greater efficiency and expression. This feature is made possible by the data pipelines that the namespace gives you access to. It makes fast, flexible, easy to use and synchronized data pipelines possible.

TensorFlow truly become an acclaimed ML framework that is way easier to develop effortlessly while leveraging the power of its own ML capabilities with relative ease!

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