TensorFlow Tutorial Index

Machine learning (ML) has become a significant part of almost all modern technologies. Understanding machine learning is challenging, but it is less daunting to train its model or implement it in real. The ease of implementing machine learning is all because of the programmer-friendly frameworks. TensorFlow is one such framework that helps easily acquire data, train different ML models, perform prediction operations, and refine future results. This tutorial will give you a comprehensive understanding of TensorFlow, its benefits, and its functions.

What is TensorFlow?

TensorFlow is a well-known machine learning and deep learning framework developed by tech giant Google to implement the machine learning concept in the easiest way possible. Google developed TensorFlow to compute extensive numerical calculations without considering deep learning (DL). But later, it helped in rendering Machine Learning (ML) and Deep Learning (DL) algorithms. It can deal with high-level data and numerical computation, along with large-scale supervised and unsupervised learning. TensorFlow can intake large data sets for training ML and DL models. The entire framework uses Python to deliver a suitable front-end API so that users can build applications with the framework. The core engine and functionality of the framework execute applications through high-performance C++.

TensorFlow is composed of two different words, Tensor and Flow, where a tensor is generalized vectors and matrices of higher dimensions, and the term flow defines the stream of data in operation. It has a broad, flexible ecosystem of tools, libraries, and community-driven free and open-source resources that helps researchers advance cutting-edge technology in machine learning and let developers quickly build and deploy machine learning-driven applications. With the help of TensorFlow, developers can develop image recognition, hand-written characters with digit classification, recurrent neural networks, sequence-to-sequence models for machine translation, recurrent neural networks, natural language processing, PDE (Partial Differential Equation) operations, simulations, etc.

History of TensorFlow

A few years back, the potential of deep learning and machine learning exceeded exponentially when products and machines started learning on their own as developers supplied extensive data in specific algorithms. Google has noticed it as an opportunity and decided to upgrade deep neural networks to render better services. They decided to use this self-learning ability in their products like Google search engine, Gmail, and Google Photos. Soon Google Brain developed the framework TensorFlow that enabled Google researchers, engineers, and developers to leverage it and build supervised and unsupervised algorithms. Once it got approved and scaled, the whole world started loving it. It was released under the Apache Open Source License and was first introduced in 2015. The first stable version came in mid-2017.

Benefits of Using TensorFlow

This ML and DL framework gives many benefits to the developers. Some of the essential benefits are:

  1. Open-source: Since this framework is open source, it is available to all. Developers and researchers can use it on any system and platform and check the source code or even modify it if needed.
  2. High scalability: Almost all ML and DL operations of any size and capacity can be performed using this platform. Furthermore, developers can use this framework to upgrade their capability and deploy their training model on any system, making it more scalable.
  3. Parallelism: TensorFlow can accelerate hardware (Graphical Processing Unit - GPU) through its acceleration library to leverage more processing for training models. It also can potentially implement additional distribution strategies for GPU and CPU hardware to perform parallelism.
  4. High data visualization: This framework is also known for its data visualization power by leveraging graphic libraries and approaches. Its visualization feature and TensorBoard help easy debugging of nodes and code snippets. Such techniques reduce the unnecessary effort of dwelling the entire code and seamlessly fix the neural network.