Tensorflow Course

📡 Simple and ready-to-use tutorials for TensorFlow
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TensorFlow Course

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This repository aims to provide simple and ready-to-use tutorials for TensorFlow. Each tutorial includes source code and most of them are associated with a documentation.


To support maintaining and upgrading this project, please kindly consider Sponsoring the project developer.

Any level of support is a great contribution here ❤️

Status: This project has been updated to **TensorFlow 2.3*.*

Table of Contents

Download Free TensorFlow Roadmap EBook

Slack Group

What is TensorFlow?

TensorFlow is an open-source software library for dataflow programming across a range of tasks. It is a symbolic math library, and is also used for machine learning applications such as neural networks. It is used for both research and production at Google often replacing its closed-source predecessor, DistBelief.

TensorFlow was developed by the Google Brain team for internal Google use. It was released under the Apache 2.0 open source license on November 9, 2015.


There are different motivations for this open source project. TensorFlow (as we write this document) is one of / the best deep learning frameworks available. The question that should be asked is why has this repository been created when there are so many other tutorials about TensorFlow available on the web?

Why use TensorFlow?

Deep Learning is in very high interest these days - there's a crucial need for rapid and optimized implementations of the algorithms and architectures. TensorFlow is designed to facilitate this goal.

The strong advantage of TensorFlow is it flexibility in designing highly modular models which can also be a disadvantage for beginners since a lot of the pieces must be considered together when creating the model.

This issue has been facilitated as well by developing high-level APIs such as Keras and Slim which abstract a lot of the pieces used in designing machine learning algorithms.

The interesting thing about TensorFlow is that it can be found anywhere these days. Lots of the researchers and developers are using it and its community is growing at the speed of light! So many issues can be dealt with easily since they're usually the same issues that a lot of other people run into considering the large number of people involved in the TensorFlow community.

What's the point of this repository?

Developing open source projects for the sake of just developing something is not the reason behind this effort. Considering the large number of tutorials that are being added to this large community, this repository has been created to break the jump-in and jump-out process that usually happens to most of the open source projects, but why and how?

First of all, what's the point of putting effort into something that most of the people won't stop by and take a look? What's the point of creating something that does not help anyone in the developers and researchers community? Why spend time for something that can easily be forgotten? But how we try to do it? Even up to this very moment there are countless tutorials on TensorFlow whether on the model design or TensorFlow workflow.

Most of them are too complicated or suffer from a lack of documentation. There are only a few available tutorials which are concise and well-structured and provide enough insight for their specific implemented models.

The goal of this project is to help the community with structured tutorials and simple and optimized code implementations to provide better insight about how to use TensorFlow quick and effectively.

It is worth noting that, the main goal of this project is to provide well-documented tutorials and less-complicated code!

TensorFlow Installation and Setup the Environment

alternate text

In order to install TensorFlow please refer to the following link:


The virtual environment installation is recommended in order to prevent package conflict and having the capacity to customize the working environment.

TensorFlow Tutorials

The tutorials in this repository are partitioned into relevant categories.


alternate text
# topic Run Source Code Media
1 Start-up Welcome Notebook / Python Video Tutorial


# topic Run Source Code Media
1 Tensors Tensors Notebook / Python Video Tutorial
2 Automatic Differentiation AD Notebook / Python Video Tutorial
3 Introduction to Graphs graphs Notebook / Python Video Tutorial
4 TensorFlow Models models Notebook / Python Video Tutorial

Basic Machine Learning

# topic Run Source Code More Media
1 Linear Regression lr Notebook / Python Tutorial Video Tutorial
2 Data Augmentation da Notebook / Python Tutorial Video Tutorial

Neural Networks

# topic Run Source Code Media
1 Multi Layer Perceptron mlp Notebook / Python Video Tutorial
2 Convolutional Neural Networks cnn Notebook / Python Video Tutorial


# topic Run Source Code Media
1 Custom Training ctraining Notebook / Python Video Tutorial
2 Dataset Generator dgenerator Notebook / Python Video Tutorial
3 Create TFRecords tfrecords Notebook / Python Video Tutorial

Some Useful Tutorials


When contributing to this repository, please first discuss the change you wish to make via issue, email, or any other method with the owners of this repository before making a change. For typos, please do not create a pull request. Instead, declare them in issues or email the repository owner.

Please note we have a code of conduct, please follow it in all your interactions with the project.

Pull Request Process

Please consider the following criterions in order to help us in a better way:

  • The pull request is mainly expected to be a code script suggestion or improvement.
  • Please do NOT change the ipython files. Instead, change the corresponsing PYTHON files.
  • A pull request related to non-code-script sections is expected to make a significant difference in the documentation. Otherwise, it is expected to be announced in the issues section.
  • Ensure any install or build dependencies are removed before the end of the layer when doing a build and creating a pull request.
  • Add comments with details of changes to the interface, this includes new environment variables, exposed ports, useful file locations and container parameters.
  • You may merge the Pull Request in once you have the sign-off of at least one other developer, or if you do not have permission to do that, you may request the owner to merge it for you if you believe all checks are passed.

Final Note

We are looking forward to your kind feedback. Please help us to improve this open source project and make our work better. For contribution, please create a pull request and we will investigate it promptly. Once again, we appreciate your kind feedback and elaborate code inspections.


Company: Instill AI [Website]

Creator: Machine Learning Mindset [Blog, GitHub, Twitter]

Developer: Amirsina Torfi [GitHub, Personal Website, Linkedin ]

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