A Step By Step Guide to Install TensorFlow

Deep Learning is one of the Hottest topics of 2020 and for a good reason. The advancements in the Industry has made it possible for Machines/Computer Programs to actually replace Humans. Artificial Intelligence is going to create 2.3 million Jobs by 2020 and a lot of this is being made possible by TensorFlow. So, in this Install TensorFlow article, I’ll be covering the following topics:

• What is TensorFlow?
• TensorFlow Applications
• TensorFlow Installation Process

What is TensorFlow?

TensorFlow is Google’s Open Source Machine Learning Framework for dataflow programming across a range of tasks. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays ( tensors) communicated between them.

Tensors are just multidimensional arrays, an extension of 2-dimensional tables to data with a higher dimension. There are many features of TensorFlow which makes it appropriate for Deep Learning.

TensorFlow Applications

Now TensorFlow has helped a lot of companies built world-class models to solve real problems. So, before we install TensorFlow, let’s have a look at some of the applications of it.

Airbnb: It improves the guest experience by using TensorFlow to classify images and detect objects at scale.

Coca-Cola: The advancements in Tensorflow enabled Coco-Cola to finally achieve a long-sought frictionless proof-of-purchase capability.

GE Healthcare: GE trained a Neural Network using TensorFlow to identify specific anatomy during brain MRIs to help improve speed and reliability.

Twitter: Twitter used TensorFlow to build their “Ranked Timeline”, allowing users to not miss any tweets even if they follow a thousand other users.

Install TensorFlow: Steps

There are few Pre-requisites before we install Tensorflow:

• Install Python

• Install Pip

Pip is already there in python 3.5.x onwards.

• Set up Virtual Environment

To set up the Virtual environment:

After this use the command

And, you’re done. Go ahead, after you install TensorFlow, just import it and start using it’s amazing deep learning capabilities and create something new.

With this, we come to the end of this blog on Data Science vs Machine Learning. With this, we come to the end of this article. If you have any queries regarding this topic, please leave a comment below and we’ll get back to you. If you wish to check out more articles on the market’s most trending technologies like Python, DevOps, Ethical Hacking, then you can refer to Edureka’s official site.

Do look out for other articles in this series that will explain the various other aspects of Data Science.

1.Data Science Tutorial

2.Math And Statistics For Data Science

3.Linear Regression in R

4.Data Science Tutorial

5.Logistic Regression In R

6.Classification Algorithms

7.Random Forest In R

8.Decision Tree in R

9.Introduction To Machine Learning

10.Naive Bayes in R

11.Statistics and Probability

12.How To Create A Perfect Decision Tree?

13.Top 10 Myths Regarding Data Scientists Roles

14.Top 5 Machine Learning Algorithms

15.Data Analyst vs Data Engineer vs Data Scientist

16.Types Of Artificial Intelligence

17.R vs Python

18.Artificial Intelligence vs Machine Learning vs Deep Learning

19.Machine Learning Projects

20.Data Analyst Interview Questions And Answers

21.Data Science And Machine Learning Tools For Non-Programmers

22.Top 10 Machine Learning Frameworks

23.Statistics for Machine Learning

24.Random Forest In R

25.Breadth-First Search Algorithm

26.Linear Discriminant Analysis in R

27.Prerequisites for Machine Learning

28.Interactive WebApps using R Shiny

29.Top 10 Books for Machine Learning

30.Unsupervised Learning

31.10 Best Books for Data Science

32.Supervised Learning

Originally published at https://www.edureka.co on April 22, 2019.