25 TensorFlow Projects Ideas for Beginners to Practice in 2024

Tensorflow Projects for Beginners to Practice with Source Code

25 TensorFlow Projects Ideas for Beginners to Practice in 2024
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Developed by the Google Brain Team, TensorFlow is an open-source deep learning framework that helps machine learning engineers and data scientists build models and deploy applications easily. With TensorFlow, getting started, building models, model training, and debugging is made easy with access to high-level APIs like Keras. TensorFlow is equipped with features, like state-of-the-art pre-trained models, popular machine learning datasets, and increased ease of execution for mathematical computations, making it popular among seasoned researchers and students alike. Currently, TensorFlow has a market share of 3.56%, with more than 1910 companies already using it. Some of the largest conglomerates like Uber, Airbnb, NVIDIA, Intel, and, quite naturally, Google use TensorFlow, consequently making using it a skill that is increasingly finding its way into job requirements for most of the data related job roles be it - data scientists, deep learning engineers, machine learning engineers, or AI engineers.


Tensorflow Transfer Learning Model for Image Classification

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Now that we have emphasized (although perhaps not strongly enough!) why knowledge of TensorFlow is so valuable in the Artificial Intelligence domain let us get started with a few cool TensorFlow projects idea that.

25 TensorFlow Projects Ideas for Beginners to Practice in 2024

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Even though the ease of use of this platform is perhaps what makes it so popular, TensorFlow can be a little daunting to just dive into, especially if you are a complete beginner. But one needs to start somewhere, so the best way to master TensorFlow would be to learn it by doing.  To help you with that, we have cherry-picked a couple of easy TensorFlow projects that should be a walk in the park with just a little effort. So, without further ado, let’s get started!

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TensorFlow Projects for Beginners

In this section, you will explore a list of beginner tensorflow projects for individuals who are new to the this popular framework in data science.

1. Detecting Spam using TensorFlow

Detecting Spam using TensorFlow

If you’ve ever used Gmail, you must be familiar with its uber vigilant spam detection. Claimed to block over 99.9 percent of phishing emails and malicious software from reaching your inbox, this feature has made the Google Suite all the more desirable for its users. Therefore, the first TensorFlow project and perhaps the most familiar on the list will be building your spam detection model!  

While this model building may not achieve Google-standard performance, creating a basic spam detection model with TensorFlow, which forms the backbone of Google’s spam detector, is pretty simple. A Recurrent Neural Network trained with a data set like the Spam Text Message Classification should serve the purpose pretty well.

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2. Image Classification with TensorFlow

Image classification essentially involves classifying an image into a fixed number of classes based on its content. Airbnb, which features millions of homes for travelers, uses image classification to ensure their guests get what they are looking for by categorizing photo listings into different room types to ensure that the listings meet standards and verify the correctness of the information. You could consider starting with deep neural network (DNN) models such as VGG, ResNet, and Inception to build an image classification model. Many pre-trained deep neural networks are available on TensorFlow.

Image Classification TensorFlow Project

However, you will need to consider using transfer learning techniques or retrain the entire model depending on the data set at your disposal. Either way, you will need to modify the last few layers of the neural network to meet your output dimension requirements and re-train the DNN to some extent until it can achieve satisfactory performance.  For this TensorFlow project, you could jump right into a multi-class classification problem with this dataset or start with a simple cat dog classification problem using this dataset.

Here’s an interesting solved TensorFlow Project on How to Build a Similar Images Finder that you might be interested in checking out.

3. Optical Character Recognition using TensorFlow

Optical Character Recognition or OCR has a myriad of uses as it allows to extract text in images (even that which is handwritten) into text format. You must have come across many software applications that achieve document dewarping, edge detection, and even online and offline optical character recognition. Kingsoft WPS, built with TensorFlow, is one of the tools in the market which has all these functionalities.  

Although there are entire applications based on it, ironically, creating your own Optical Character Recognition system isn’t all too hard. Actually, for this project, you could go a step beyond and try handwritten text recognition using the ResNet model in Keras/TensorFlow with the popular publicly available MNIST dataset

Check out this solved project to learn How to Build an OCR from scratch?

Intermediate Projects in TensorFlow 

If the beginner projects were smooth sailing, or perhaps just not ‘cool’ enough, be assured that the next few tensorflow deep learning projects will not disappoint, for the more challenging the job, the sweeter the reward!

4. AR Face Filters using TensorFlow

ModiFace, which the L’Oreal Group acquired in 2018, allows users to see how the product will look on them before purchase rather than just browsing through product photos. The first intermediate TensorFlow project, therefore, is to set up your own simple face landmark detection application. For this, you could simply use the Media Pipe Facemesh model and create your custom filters and cool AR effects (The difficulty will depend on how complex you make them...). You can either use static images (for example https://www.kaggle.com/laurentmih/aisegmentcom-matting-human-datasets/) or (even better) work with your front-facing camera to apply effects in real-time.

5. Neural Style Transfer using TensorFlow

Initially introduced in Leon A. Gatys’ paper, “A Neural Algorithm of Artistic Style,” neural style transfer has taken the world by storm and has caught the attention of many. Essentially requiring using a reference style image, such as an artwork by a well-known artist or just some random graffiti or pattern, and the input image that contains the content you want to style, you could obtain an image created in the style of the style image.

Neural Style Transfer using TensorFlow

https://www.coursera.org/learn/convolutional-neural-networks/lecture/SA5H8/what-is-neural-style-transfer

For instance, you could use a picture of the Stanford University Campus and paint it with the popular Van Gogh’s Starry Night style as shown in the image above. 

To accomplish Neural Style Transfer, you need to build a Convolutional Neural Network and decide on the weightage you want to allot for the content cost component and the style cost component to achieve the desired effect. While there is no shortage of images available for this purpose, you could start by considering some Kaggle Datasets like the one on https://www.kaggle.com/karthikayanmailsamy/tamil-nst?select=TamilStyleImages

FUN FACT!

Google’s very own DeepDream software, developed by their engineer Alexander Mordvintsev, which can find and enhance patterns in images to create a dream-like appearance, is based entirely on Neural Style Transfer. You can catch DeepDream in all its hallucinogenic glory on Foster the People's music video for the song "Doing It for the Money”!!!

6. Sudoku Solver using TensorFlow

Using AI to solve puzzles and play games is becoming increasingly common with more and more big names like Google and Microsoft funding research towards developing AI players for digital games. Sounds pretty cool, right? For someone who is fairly new to this area building a Sudoku Grid Solver might just be the place to start. One feasible way to build a Sudoku Solver using TensorFlow involves training a Convolutional Neural Network to learn the simple mathematical rules. You could use a dataset such as this one https://www.kaggle.com/bryanpark/sudoku for this purpose. To amp up the difficulty, you could also attempt reading the puzzle itself from images such as the ones provided in this dataset https://www.kaggle.com/niharikavats97/sudokuscreenshots (with some data cleaning ofcourse). Individuals interested in Tensorflow gaming projects should not miss out on this section.

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Advanced Projects on TensorFlow 

The difficulty of the various cool tensorflow projects in this section comes not from their difficulty to be built (you will notice that you will be able to achieve decent results even with the same amount of effort you put in the previous few) but from the fact that for many of these use cases, mediocrity might just not be acceptable. Therefore while you could use shortcuts or readily available models, the best performance to satisfy your exact requirements might require a tad bit more effort...

7. Recommender Systems (Tweet Ranking) using TensorFlow

Recommender Systems (Tweet Ranking) using TensorFlow

Whether it be shopping websites or streaming applications, sorting things for relevance, or giving them under the beloved ‘You may like this is becoming increasingly mainstream, so much so that you may not know a service that doesn’t do this (after all good recommender systems can make or break a company). 

The well-loved Twitter is no stranger to these ranking systems. Having moved on from the age-old practice of presenting Tweets in the reverse-chronological order, Twitter now uses the TensorFlow framework for tweet ranking to make sure their users never miss the most relevant tweets. They consider a combination of factors like the content of the tweet, its recency, the number of Retweets or likes, the Tweet’s author and an individual's connection to them, and even activity (how often and how heavily one has been using Twitter) to make sure you never miss out. 

Equipped with the arsenal of Twitter’s ever-handy Developer APIs (https://developer.twitter.com/en)  and TensorFlow, you could implement this with a deep learning-based approach using possibly Recurrent Neural Networks (or TensorFlow Recommenders (TFRS) library specialized for building recommender system models) or even explore Gradient Boosted Decision Trees (which Twitter itself observed was increasingly popular at RecSys 2020). However, tread carefully here, for much care and effort goes into building recommender systems and modifying them, and you can imagine why… 

8. Auto Classification of Shopping Products using TensorFlow

Although classification tasks are usually considered fairly basic, the complexity of this project comes from the nature of the data or the lack of it. Naver Shopping is a shopping portal service that has been using TensorFlow to achieve the auto-classification of over a dozen million new products a day into their predefined categories, a task which would have otherwise been enormous if not impossible. Achieving this however has not been a cakewalk, for the seller categories and details do not always match with the categories defined by Naver. This has compelled them, therefore, to use a combination of techniques like CNN-LSTM Model for the product name, MobileNet for the product image, and even simple Convolutinoal Neural Network model among other things. 

To start off with your own product classification task you could consider using a few publicly available datasets (https://www.kaggle.com/c/retail-products-classification/data) and then decide on which model or algorithm (or an ensemble of them)  would work best for your needs.

9. Gesture Controlled Game using TensorFlow

Gesture-controlled devices are becoming popular and the market is expected to be valued at USD 25551.99 Million by 2025. Although the slightly less known GestureTek is a patent-holder and world-leader in camera-enabled gesture-recognition technology for presentation and entertainment purposes, other giants like Microsoft, Sony, and Intel are also making their presence known in this industry.

Building a gesture-controlled game can, therefore, be a fun but useful way to introduce yourself to this area. The simplicity or complexity that this project involves will depend entirely on how easy or hard you choose to make it i.e. you could use gesture control for playing Breakout or even Chess!  You can achieve this by training a Convolutional Neural Network or even using the Object Detection API depending on your requirements.

10. Reinforcement Learning Based Game Player using TensorFlow

DeepMind, which was acquired by Google in 2014, designed AlphaGo Zero which has now gone on to beat the highest-ranked Go player in the world. The specialty of AlphaGo Zero is that it uses reinforcement learning and hence its training involves learning the game by playing against itself. Delving into reinforcement learning can be particularly desirable since it has not only been used to train bots to play games but has also enabled robots to learn to walk, opening up a world of possibilities. The last tensroflow project, therefore, is building a reinforcement learning-based game player for a game of your choice! You might want to consider using a Deep or a Double Deep Q-Learning model depending on the game you want your program to play.

Practice solved end-to-end TensorFlow Projects to solve challenging, real-world challenging business problems with machine learning. 

Open Source Projects using TensorFlow

This section has open source TensorFlow projects that you can contribute to if you have mastered this Python library.

11. TensorFlow Hub

TensorFlow Hub is a library for reusable machine learning modules. It contains a large number of pre-trained models that can be used for tasks such as image classification, natural language processing, and more.

12. TensorFlow.js

TensorFlow.js is a JavaScript library that allows developers to build and train machine learning models directly in the browser. It is built on top of TensorFlow and can be used for a wide range of applications.

13. TensorFlow Extended (TFX)

TFX is an end-to-end platform for deploying machine learning models into production. It includes tools for data ingestion, preprocessing, model training, and deployment.

TensorFlow Computer Vision Projects

Check out this section if you are interested in computer vision based good projects in TensorFlow.

14. Object Detection using TensorFlow

Object detection goes beyond standard image classification by detecting instances of objects within the said image. For example, while an object classification system would sort a photograph containing three cats into the Cat category, object detection would output bounding boxes for each of the cats. 

Airbnb has been using object recognition to detect amenities and ensure that they provide convenience for their guests. They have built their very own customized object detection model to suit their needs. While building and training a custom model is well beyond the scope of a beginner project, you could most definitely use the TensorFlow object detection API, with its collection of pre-trained models, for beginners. For testing purposes, you could use one of the many available datasets such as this one: 

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15. Face Recognition using TensorFlow

Face Recognition TensorFlow Project

Facebook’s face recognition model DeepFace has shown a performance that sometimes exceeds that of humans. Although a model of DeepFace implemented using Keras has been made publicly available, you can alternatively consider Multi-Task Cascaded Convolutional Neural Network to extract faces and use the Keras pre-trained FaceNet model to recognize celebrities. You can use the following machine learning datasets for testing these models -

https://www.kaggle.com/dansbecker/5-celebrity-faces-dataset, https://www.kaggle.com/danupnelson/14-celebrity-faces-dataset)

16. Deep Learning in Medical Imaging using TensorFlow

One of the biggest players in the healthcare industry, GE Healthcare, has been using the TensorFlow library with the Keras interface to develop and test ideas for their product AIRx or Artificial Intelligence Prescription. AIRx uses 2D and 3D Convolutional Neural Networks (CNN) to allow for Intelligent Slice Placement, so that the scan planes for a brain MRI can be reduced by 40% to 60%, besides contributing to reduced errors and improved accuracy.

A good way to start towards applying deep learning in medical imaging would perhaps be taking up a task such as brain tumor classification (https://www.kaggle.com/sartajbhuvaji/brain-tumor-classification-mri)

Even though Convolutional Neural Networks are the common go-to method for image recognition it might be advisable to go over the most recent literature in the area to draw inspiration on some modifications you could incorporate. This project is a must if you are looking for small tensorflow healthcare projects.

NOTE: It cannot be stressed enough here that achieving mediocre results isn’t a challenge with this task, however, owing to the criticality of the task at hand, perfection( hopefully beyond human capability) is what must be sought. And this is where the difficulty comes…

TensorFlow NLP Projects

If you are looking for good projects for learning TensorFlow and NLP, then check out the projects in this section.

17. Speech Recognition

The ubiquity of Speech Recognition systems like Apple’s Siri, Amazon’s Alexa, and Microsoft’s Cortana should suffice to emphasize the importance speech recognition systems hold. With a little research, implementing your own basic speech recognition system should be fairly easy. 

One possible way of achieving this is training a CNN with the MFCC spectrograms obtained from the raw data. You can use this publicly available training dataset for this purpose: https://www.kaggle.com/c/TensorFlow-speech-recognition-challenge/data

NOTE: This project has perhaps been oversimplified here. However, if you are up to take up a harder task (like actual Natural Language Processing) you will notice that there is much more to be explored in this domain.

18. Intent Recognition using TensorFlow

Intent Recognition is becoming increasingly important as we indulge in the digital space almost as much as we do in the physical world around us. For example, Facebook developed RoBERTa, a modified version of the BERT model, to tackle content moderation and has claimed to have been able to increase the blocking of harmful posts by 70%. With the advent of BERT, intent recognition has been drastically simplified and improved however its value has by no means diminished. Developing your own intent recognition system will, therefore, be a brilliant project to undertake. You can start off with the 12/768 (BERT-Base) model, which can be downloaded from this link - https://storage.googleapis.com/bert_models/2020_02_20/uncased_L-12_H-768_A-12.zip

19. Sentiment Analysis

This project involves building a system that can use the Tensorflow framework to build a model that can classify the sentiment of a given text as positive or negative. You can prepare the dataset on your own by scraping twitter for the movie reviews of a specific movie of your choice. Once you have the dataset ready, implement NLP methods to polish the dataset before serving it as an input to the training model. To perform the classification task, you can use a deep learning model such as neural network classification model.

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Python TensorFlow Projects on GitHub

In this section, you will find tensorflow projects with source code on GitHub.

20. Real-Time Voice Cloning

This project allows users to clone a voice using audio files in real-time, which means that the output voice can be generated immediately as the user speaks. The project uses deep learning techniques to create a text-to-speech (TTS) system that can generate speech in a cloned voice. There are three stages in this project. The first stage involves the creation of digital representation of a voice using a sample audio file. The next two stages use this digital representation build an audio sample for that voice for any given text. The project has two main components: Speaker Encoder and Synthesizer. To clone a voice in real-time, the user first records a few minutes of audio from the target speaker, which is used to train the speaker encoder. Once the speaker embedding is obtained, the user can input any text, and the synthesizer generates speech in the cloned voice.

GitHub Repository: https://github.com/CorentinJ/Real-Time-Voice-Cloning 

21. Neural Style

Prisma is one of the most popular mobile applications that use artificial intelligence to edit the input photos. The source code of the Prisma app is available on GitHub for free and goes by the name fast neural style. In this project, you will learn tensorflow implementation of such application. The application basically combines the style of one image over the other and lets you control the percentage of the style that has been imprinted on the input image.

GitHub Repository: https://github.com/cysmith/neural-style-tf 

22. DeepSpeech

DeepSpeech is a TensorFlow project developed by Mozilla that aims to create an open-source automatic speech recognition (ASR) engine that is accessible to everyone. The project uses deep learning techniques to train a neural network that can convert spoken words into text.

It is a powerful and flexible ASR engine that can be trained on any language and can be adapted to different domains and applications. The project is open-source, which means that anyone can contribute to its development and use it for their own projects. The use of TensorFlow makes it easy to train and deploy the models on a variety of platforms, including desktops, servers, and mobile devices.

GitHub Repository: https://github.com/mozilla/DeepSpeech 

Want to start your journey in Machine Learning with R but don't know how? Start working on these Machine Learning Projects in R for Beginners today!

TensorFlow Projects using Kaggle Datasets

23. Face Emotion Recognition

Face Emotion Recognition

Kaggle has the Facial Expression Recognition Challenge dataset that you can use to build a face emotion recognition system. The dataset contains facial images in grascale of people with different emotions such as Angry, Disgust, Fear, Happy, Sad, Surprise, and Neutral. The emotions are represented using numbers 0 to 6. Using TensorFlow, one can train a deep learning model to recognize emotions from the facial images and then use it for prediction. One can use such a system to understand the customer experience from their facial expressions.

24. Speech Emotion Recognition

Kaggle provides various datasets for speech emotion recognition, such as the Ryerson Audio-Visual Database of Emotional Speech and Song dataset. This dataset contains audio recordings of people speaking with different emotions such as neutral, calm, happy, sad, angry, fearful, disgust, and surprised. The goal in this project is to use TensorFlow for building deep learning models that can recognize emotions from speech signals. These models can have applications in various fields such as healthcare and customer service, where understanding human emotions can be crucial.

25. Time Series Forecasting

Kaggle has a New York City Taxi Trips dataset that contains data about taxi rides in New York City from 2009 to 2015. The dataset includes information such as the pickup and dropoff locations, the pickup and dropoff times, the distance of the ride, and the fare amount. This dataset is often used for building machine learning models that can predict taxi fares or the number of taxi pickups/dropoffs in a given time period. You can use TensorFlow to build deep learning models that can forecast the number of taxi pickups in a specific area or predict the fare amount of a given ride based on the pickup and dropoff locations and other variables.

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FAQs

1. What can you build with TensorFlow?

With TensorFlow, you can build a wide range of machine learning models such as deep neural networks, convolutional neural networks, recurrent neural networks, and more. These models can be used for various applications such as image recognition, speech recognition, natural language processing, and time-series forecasting. 

2. Do professionals use TensorFlow?

Yes, TensorFlow is widely used by professionals in the industry, including data scientists, machine learning engineers, and researchers. TensorFlow's flexible and powerful framework, along with its extensive community support, has made it a popular choice for building and deploying various deep learning and machine learning models.

3. Which big companies use TensorFlow?

Many tech giants such as  Google, Intel, NVIDIA, Airbnb, Twitter, SAP, and Uber have adopted TensorFlow for their machine learning and AI projects. Google, the creator of TensorFlow, uses it extensively for its own products and services, such as Google Photos and Google Translate. TensorFlow's versatility and scalability make it a popular choice among the data science professionals.

 

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