In this post, we are going to get a basic understanding of:

  • What is TensorFlow?
  • What is offered in the context of machine learning?
  • Tensorflow used in applications
  • Types of TensorFlow


It is a free and open-source platform for high-performance numerical computation, specifically for machine learning (ML) and Deep learning (DL). It has a flexible architecture and can be deployed across a variety of platforms like CPUs, GPUs, and Google TPUs as well as mobile and edge devices.

Tensorflow in Machine Learning:

Tensorflow makes it easy to build and deploy Machine Learning solutions. So, this means that it’s not a simple framework just to build a machine learning project but a complete ecosystem that provides tools at each stage of the entire machine learning workflow:

  • Data preprocessing to feature engineering
  • Model training to model serving
  • Data Pipelines to model inference

TensorFlow Used in Application:

  • Search Engines
  • Text Translation
  • Image Captioning
  • Interpreting
  • Recommendation Systems
  • Weather forecasting

A tensor is simply a typed multi-dimensional array. It can be 0 to N-dimensional.

Types of Tensorflow:

There we have different types of Tensors

Zero-dimensional: Which we call the Scalar. As you can see on the screen it has only the magnitude.

One-dimensional: which we usually call the Vector, it has the magnitude and the direction as you can see on the screen.

Two-dimensional: which we usually call a Matrix, this can be represented as a table of numbers.

Three-dimensional: which is also a matrix but it can be represented as a cube.

N-dimensional: represented as a matrix.

Great, at this stage you should be familiar with TensorFlow, what it is, why we should use it along the most basic concept of Tensors.

Feb. 8, 2022
Machine Learning

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