Home » Data Science » Data Science Tutorials » **TensorFlow Tutorial**

Basics

TensorFlow is a machine learning and deep learning library developed by Google and it went public in the year 2015. It is currently the most used deep learning library in the market and its very user-friendly. This library is used to develop machine learning models and implement all the important and unachievable fascinating artificial intelligence ideas. This library is completely written in python programming language and that is the reason it's very much compatible with python. This library is used in the learning and research purpose as well as large scale production level machine learning models. Google has diversified this library and the company is working extensively on this currently is compatible with c++, JavaScript and mobile platforms such as swift programming language.

In this vast and ever-changing field of data science, where the data is increasing at a rate of knots and the challenges involving those heterogeneous types of data are also unique, so to be updated with this one need to keep on diversifying and learn new techniques and libraries.

TensorFlow is the most advanced, user-friendly library which helps solves the problem associated with the unstructured data and implement the solution is quite simple steps of python code. this TensorFlow machine learning framework is also very well documented anyone can go through the documentation and understand the working of the code presented there. Google also offers various kinds of tutorials for deep learning involving the tensor flow library.

One has to learn and get accustomed to the TensorFlow library if he/she wants to make a carrier in the field of Artificial intelligence and data science.

Currently, there are many application which we use in our day to day life which is powered by TensorFlow framework.

- Image/Voice Recognition systems
- Self Driving cars
- Recommendation systems
- Text Summarization
- Sentiment analysis of the written text
- Speech Recognition systems

Let's see an example where we will create a computational graph to multiply 2 numbers. Below is the figure of the computation performed in the tensor flow.

Importing the necessary libraries for the code that is TensorFlow as tf and numpy as np. numpy is the mathematical library provided by python.

Declaring the variables in the form of tensors as this is a graphical computation.

Declaring the 3^{rd} variable for the calculation and adding the names for the node.

Doing the final calculation and viewing the output.

To get started with the tutorial for the TensorFlow framework one has to be well versed with the python programming language and few topics of mathematics which includes linear algebra, probability, matrix calculation, and if someone has the knowledge of artificial intelligence then it will be a plus point.

This TensorFlow tutorial is developed for the python developers who want to make a carrier in the field of data science and machine learning. Or who wants to focus on the research in the field of Artificial intelligence and aim of this tutorial is the get one familiar with the various packages and methods in the TensorFlow library.

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