TensorFlow for machine learning: Everything you need to know

Machine learning is an emerging and complex IT discipline. But thanks to the advancements in technology, implementing machine learning solutions at an enterprise scale is now far less complex than it used to be. Machine learning is a specialized form of data analysis, which automates analytical building models. Machine learning has become crucial in many established and burgeoning companies. The applications and use cases of machine learning are spread across various domains such as health care, IT, cybersecurity, retail, marketing, and finance. Machine learning can enable easy yet massive data examination, perform rapid analysis, and interpret information from the data and past instances. There are several other advantages of machine learning such as easy spam detection, better customer segmentation, and product recommendations. But leveraging machine learning can be difficult. Fortunately, TensorFlow can handle the heavy lifting for you.

TensorFlow: Introduction

[tg_youtube video_id=”mWl45NkFBOc”]

Thanks to Google’s open source framework TensorFlow, using and implementing machine learning solutions is now made easy and much less complex. TensorFlow is an open source software library meant for high-performance numerical computations and enterprise-level machine learning implementations. It comes with a flexible architecture and can be used to run across various platforms and devices ranging from mobile or edge devices to clusters of servers.

How does it work?

TensorFlow allows developers to create data flow graphs, which explains how data moves through a graph or a series of processing nodes. Each node in the series represents a particular mathematical operation. All these nodes are connected using multidimensional data arrays, which are also known as tensors.

TensorFlow supports Python, R, JavaScript, Swift, and other programming languages. These programming languages provide developers with convenient ways to couple high-level abstractions for applications. Moreover, all TensorFlow applications are built on Python, which makes it easy for support and understanding.

All TensorFlow applications can be installed on small devices such as mobiles and can also be implemented on large-scale cloud clusters. Users can install TensorFlow on any of the below 64-bit systems.

  • Ubuntu 16.04 or later
  • macOS 10.12.6
  • Windows 7 or later
  • Raspbian 9.0 or later

Advantages

TensorFlow
Shutterstock

TensorFlow offers several benefits to get the most out of machine learning and deep learning. TensorFlow comes with easy-to-learn development options and a clean installation process to help you quickly get the most out of machine learning and deep learning.

TensorFlow comes with these major benefits:

Abstraction

Abstraction is one of the most important and major advantages offered by TensorFlow. Dealing with machine learning was considered difficult because of the extensive complexity and detailing needed in implementing algorithms. But thanks to TensorFlow, developers only need to focus on the logic — all the underneath details of development are taken care of by the TensorFlow.

Visualization

TensorFlow
Shutterstock

TensorFlow comes with a full state of visualization tools that makes it easy for developers and analysts to understand the process and applications. These visualization tools also enable easy debug, deploy, and optimization of applications. TensorFlow supports various types of histograms, graphs, audio and videos, and images for easily understanding the neural networks within the TensorFlow.

Support and usage

TensorFlow comes with good support from the open source community. And Amazon has a provision to use TensorFlow through AWS SageMaker. There are several TensorFlow-based applications and services that also help make it one of the most resourceful frameworks to adapt and implement machine and deep learning. TensorFlow has a very large, extensive, and growing community of active users.

Easy development

TensorFlow supports mobile development, which features reduced code footprint and other mathematical and statistical tools to facilitate mobile platforms such as Android and iOS.

Documentation

As mentioned above, TensorFlow comes with extensive support. The support includes detailed documentation and tutorials, which can boost the development process.

Support for Keras

Keras is an extremely popular high-level API for building, deploying, and using deep learning models. Keras provides efficient and faster prototyping and research and is used in various industries. TensorFlow supports Keras, which is now more tightly integrated into the TensorFlow platform. This makes it easy for the developers to get started with the machine learning using TensorFlow.

Use of machine learning is now powered by several products readily available on the market. While TensorFlow is one such enabler of machine learning at every level, it offers various advantages and benefits compared with the other platforms such as Microsoft’s Cognitive Toolkit, Apache MXNet, and PyTorch.

Leave a Comment

Your email address will not be published.

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Scroll to Top