The guided projects on TensorFlow by Coursera is easy and simple to follow. We’ve listed some of the best TensorFlow projects for beginners and freshers. Take these projects to gain more hands-on experience in using TensorFlow.
These TensorFlow sample projects can be taken by anyone who has recently completed the TensorFlow course. The more practice you do, the more experience you become in TensorFlow. The cost of these basic TensorFlow projects in Coursera will cost $10 for each project course.
Why is it Important?
Just by completing the TensorFlow online course wouldn’t help your resume or portfolio. If you highlight some of the projects, you worked using TensorFlow, which gives you the upper hand.
In order to excel in polishing your skillset, we recommend that you sign up for more than a few guided projects. Also, feel free to check out all the TensorFlow based projects in Coursera.
Now let’s check out all the best TensorFlow projects for beginners available in Coursera.
Best TensorFlow Projects for Beginners 2020
1. Basic Image Classification with TensorFlow
In this TensorFlow project, using Keras with TensorFlow, you solve a basic image classification problem. It is perfect for beginners to start practicing TensorFlow to understand the basics of Neural Networks for Image classification.
By the end of this project, you will create, train, and evaluate a Neural Network model to predict digits from hand-written images. The project instructor will provide detailed instructions so that beginners can easily understand.
2. Predicting House Prices with Regression using TensorFlow
This beginner’s TensorFlow project is 2 hours long. Here you use neural networks to solve regression problems. Since TensorFlow is a huge topic, not every topic will be covered in your online course. Hence these sample projects will help beginners to intermediate users to gain hands-on knowledge.
By completing this project, you will train and evaluate the neural network model to predict house prices. Additionally, you learn various libraries and functions that will help in your future projects.
3. Regression with Automatic Differentiation in TensorFlow
To successfully participating in this project, you should know Python, Linear Regression, and Gradient Descent. In this project, you will learn tensor constants, automatic differentiation, and tensor variables. Then solve a linear regression problem using automatic differentiation.
By the end of this 90-minute project, you will have a better understanding of Machine Learning algorithms. Other topics covered here are persistent tape, generating Data for Linear Regression, and watching Tensors.
4. Neural Network from Scratch in TensorFlow
If you know Python programming, basics of TensorFlow, concepts of Neural Networks, and gradient descent, then you can take this project. The duration of the Neural Network from Scratch in the TensorFlow project is 2 hours. This is an intermediate level project on TensorFlow.
Here you’ll implement gradient descent algorithm using automatic differentiation. Using the neural network implementation, you will be trained to solve the multi-class classification problem. To have a better understanding of the code, the instructor explains what each line of code does.
5. Neural Style Transfer with TensorFlow
In this sample TensorFlow project, you will finally understand the working of Neural Style Transfer. Then you will be able to stylize a given content image by applying for Neural Style Transfer. More than 2.2k students have taken this project to practice TensorFlow concepts.
The guide from the instructor is a step-by-step and clear explanation and is easy to understand. You will run a training loop to optimize a proposed image that retains content features. This project is helpful for Machine Learning and Deep Learning practitioners.
6. Basic Sentiment Analysis with TensorFlow
The objective of this TensorFlow project would be to solve text classification and sentiment analysis with neural networks. Using Keras with TensorFlow in the backend, you will learn to solve basic sentiment analysis problems.
Over 3.4k students have enrolled in this TensorFlow sample project. By the end, you will have learned to predict movie reviews. This project will pave a path to prepare you to become an AI engineer. It is a simple and informative project on TensorFlow.
7. Deploy Models with TensorFlow Serving and Flask
In this guided project on TensorFlow, you are taught to deploy TensorFlow models using TensorFlow Serving and Docker. Then you will proceed to create a web application using Flask to interface to a served model. This is a 2 hour long, simple TensorFlow project.
The pre-configured cloud desktops are provided in every project course by Coursera. These cloud desktops have all the necessary tools pre-installed so that you wouldn’t wast any time. Over 3k students have benefitted from this project to get more hands-on practice labs.
8. Using TensorFlow with Amazon Sagemaker
In the AWS Amazon Sagemaker ecosystem, you will train and deploy image classifiers. In AWS Sagemaker, several Machine Learning algorithms are ready to use. In this project, you will learn to create a custom script for Sagemaker.
Using Amazon Sagemaker, you will learn to train a TensorFlow model and deploy them to solve problems.
This is an advanced level project in TensorFlow with AWS Sagemaker. If you’ve some experience on the AWS cloud, this will have major value addition to your resume and portfolio. If you want to learn cloud computing, check out some of the easiest cloud computing courses.
9. Image Noise Reduction with Auto-encoders using TensorFlow
In this TensorFlow project, you will understand how auto-encoders work and then learn to create auto-encoder. Then you will apply the auto-encoder to reduce the noise in a given image. Beginners can leverage this project to learn new concepts, which is great for beginners.
Auto-encoding can be used to compress lossy data where the compression is dependent on the given data. You learn the essential topics on TensorFlow by working on this project.
10. Avoid Overfitting Using Regularization in TensorFlow
In this TensorFlow project for beginners, you’ll understand how to avoid over-fitting with dropout regularization and weight regularization. The basics of dropout regularization and weight regularization are taught here to solve an image classification problem.
By completing this project, you will have learned to create, train, and evaluate a Neural Network model. This is an intermediate level project that would take 2 hours to complete the project.
11. Real-time OCR and Text Detection with Tensorflow, OpenCV and Tesseract
Here you are trained to train a Tensorflow model to recognize a Region of Interest (ROI) in an image or in a frame of a video. Then using OpenCV, you can extract and enhance image segments. You can perform all this in Windows PC and in Linux with few mods.
Using IDLE, you will learn to write a simple script to get webcam input, scan video, or read text into our output. This Coursera guided project on TensorFlow can be completed in 2 hours.
Summary: Projects using TensorFlow Worth it?
Totally. The hands-on TensorFlow practice that you get in these projects become invaluable when you get to work on real-world problems. These are the best TensorFlow projects for beginners that should be taken after completing a TensorFlow course.