TensorFlow has only supported 64-bit Python 3.5.x or Python 3.6.x on Windows
When we download the Python 3.5.x version, it comes with the pip3 package manager. (Which is the program that we are going to need for our users to install TensorFlow on Windows).

Step 1: Download Python from the below link: https://www.python.org/downloads/release/python-352/

After that,

Step 3: We will be brought to another page, where we will need to select either the x86-64 or amd64 installer to install Python.

We use here Windows x86-64 executable installer.

Now, Python is installing successfully.

Step 4: For this tutorial, I’ll be choosing to Add Python 3.5 to PATH.

Step 5: Now, we will be able to see the message “Set-up was successful.” A way to confirm that it hs installed successfully is to open your Command Prompt and check the version.

What is pip?

pip is known as a package management system which is used to install and manage the software package, which is written in Python or any other languages. pip is used to download, search, install, uninstall, and manage the 3rd party python package. (pip3 is the latest version of it which comes with new Python 3.5.x version that we had just downloaded)

Installing our TensorFlow

Once we have downloaded the latest version of Python, we can now put our finishing touches by installing our TensorFlow.

Step 1: To install TensorFlow, start the terminal. Make sure that we run the cmd as an administrator.

If we do not know how to run your cmd as an administrator

Here’s how we can run in our cmd as an administrator.

Open the Start menu, search for cmd, and then right-click on it and Run as an administrator.

Step 2: Once we are done with that, then we have to write the command in command prompt for finish installing Tensorflow in our Windows.

Enter this command:

C:\pip3 install -upgrade tensorflow

Now, TensorFlow is successfully installed in our system.

Testing our TensorFlow

Here, we try and prove whether our new TensorFlow works smoothly without any problems.

Below is an example that you can write to the test.

TensorFlow is successfully working now.

Otherwise, If we are getting any problem to run the program, then we have to install Microsoft Visual C++ 2015. And set up in our system then the TensorFlow will be run in the system.

We can download Microsoft Visual C++ 2015 from the below link: https://www.microsoft.com/en-us/download/confirmation.aspx?id=53587

We can download from here.

Choose the vc_redist.x64.exe on the page and click on “Next” after that it will be downloaded.

At last, it will successfully be installed in our system.

So, this brings us to the end of blog. This Tecklearn ‘How to install TensorFlow Through pip in Windows’ blog helps you with commonly asked questions if you are looking out for a job in Artificial Intelligence. If you wish to learn Artificial Intelligence and build a career in AI or Machine Learning domain, then check out our interactive, Artificial Intelligence and Deep Learning with TensorFlow Training, that comes with 24*7 support to guide you throughout your learning period. Please find the link for course details:

https://www.tecklearn.com/course/artificial-intelligence-and-deep-learning-with-tensorflow/

Artificial Intelligence and Deep Learning with TensorFlow Training

About the Course

Tecklearn’s Artificial Intelligence and Deep Learning with Tensor Flow course is curated by industry professionals as per the industry requirements & demands and aligned with the latest best practices. You’ll master convolutional neural networks (CNN), TensorFlow, TensorFlow code, transfer learning, graph visualization, recurrent neural networks (RNN), Deep Learning libraries, GPU in Deep Learning, Keras and TFLearn APIs, backpropagation, and hyperparameters via hands-on projects. The trainee will learn AI by mastering natural language processing, deep neural networks, predictive analytics, reinforcement learning, and more programming languages needed to shine in this field.

Why Should you take Artificial Intelligence and Deep Learning with Tensor Flow Training?

According to Paysa.com, an Artificial Intelligence Engineer earns an average of $171,715, ranging from $124,542 at the 25th percentile to $201,853 at the 75th percentile, with top earners earning more than $257,530.
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IBM, Amazon, Apple, Google, Facebook, Microsoft, Oracle and almost all the leading companies are working on Artificial Intelligence to innovate future technologies.

What you will Learn in this Course?

Introduction to Deep Learning and AI

What is Deep Learning?
Advantage of Deep Learning over Machine learning
Real-Life use cases of Deep Learning
Review of Machine Learning: Regression, Classification, Clustering, Reinforcement Learning, Underfitting and Overfitting, Optimization
Pre-requisites for AI & DL
Python Programming Language
Installation & IDE

Environment Set Up and Essentials

Installation
Python – NumPy
Python for Data Science and AI
Python Language Essentials
Python Libraries – Numpy and Pandas
Numpy for Mathematical Computing

More Prerequisites for Deep Learning and AI

Pandas for Data Analysis
Machine Learning Basic Concepts
Normalization
Data Set
Machine Learning Concepts
Regression
Logistic Regression
SVM – Support Vector Machines
Decision Trees
Python Libraries for Data Science and AI

Introduction to Neural Networks

Creating Module
Neural Network Equation
Sigmoid Function
Multi-layered perception
Weights, Biases
Activation Functions
Gradient Decent or Error function
Epoch, Forward & backword propagation
What is TensorFlow?
TensorFlow code-basics
Graph Visualization
Constants, Placeholders, Variables

Multi-layered Neural Networks

Error Back propagation issues
Drop outs

Regularization techniques in Deep Learning

Deep Learning Libraries

Tensorflow
Keras
OpenCV
SkImage
PIL

Building of Simple Neural Network from Scratch from Simple Equation

Training the model

Dual Equation Neural Network

TensorFlow
Predicting Algorithm

Introduction to Keras API

Define Keras
How to compose Models in Keras
Sequential Composition
Functional Composition
Predefined Neural Network Layers
What is Batch Normalization
Saving and Loading a model with Keras
Customizing the Training Process
Using TensorBoard with Keras
Use-Case Implementation with Keras

GPU in Deep Learning

Introduction to GPUs and how they differ from CPUs
Importance of GPUs in training Deep Learning Networks
The GPU constituent with simpler core and concurrent hardware
Keras Model Saving and Reusing
Deploying Keras with TensorBoard

Keras Cat Vs Dog Modelling

Activation Functions in Neural Network

Optimization Techniques

Some Examples for Neural Network

Convolutional Neural Networks (CNN)

Introduction to CNNs
CNNs Application
Architecture of a CNN
Convolution and Pooling layers in a CNN
Understanding and Visualizing a CNN

RNN: Recurrent Neural Networks

Introduction to RNN Model
Application use cases of RNN
Modelling sequences
Training RNNs with Backpropagation
Long Short-Term memory (LSTM)
Recursive Neural Tensor Network Theory
Recurrent Neural Network Model

Application of Deep Learning in image recognition, NLP and more

Real world projects in recommender systems and others

Got a question for us? Please mention it in the comments section and we will get back to you.

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