Data is the new oil, and the future of the world is in the information and insights contained in data. Artificial intelligence and its subset of machine learning are data analytics techniques that have in the last decade caught the attention of the world. Much research and development have gone into advancing machine learning to crunch massive datasets to predict outcomes, and the TensorFlow framework is one innovative framework that rose out of this advancement. 

Whether you are a beginner in this profession or a machine learner considering furthering your knowledge and skills by undertaking a deep learning course, you will at one point or another use the TensorFlow framework. Organizations have widely adopted TensorFlow to help them to incorporate AI and ML concepts like computer vision, speech recognition, and image recognition into applications. 

TensorFlow is an open-source machine learning and deep learning framework developed by the Google Brain Team for application development. TensorFlow is compatible with multiple languages and is written in Python, C++, and CUDA programming languages. It makes it easy to build and deploy ML algorithms and applications with fewer lines of code. This library comes readily equipped with ML and DL pre-trained models, algorithms, ML datasets, and Keras library and uses deep neural networks to ease the processes of data acquisition, model training, and prediction. 

Top X Tensorflow Projects for Beginners

TensorFlow may be a popular and perhaps standard framework, but for beginners, mastering TensorFlow may present a steep learning curve. A good way of learning how to work with this framework is learning by doing. How about practicing with some simple beginner-level TensorFlow projects? 

Get started with these few simple projects that we have selected for you. 

1. Image Finder Classification Project 

A classic example of an image finder application is the Airbnb website which contains millions of different types of homes for travelers looking for convenient and affordable accommodation to suit their needs. This program classifies accommodation facilities based on the type of accommodation facility, living arrangement, size, and the number of rooms. 

This image classification project involves building a program that can classify images into fixed classes based on their features and content. You may start by using deep neural network (DNN) or convolutional neural network (CNN) models. Good pre-trained models you can consider using are the VGC or ResNet that you will use to build an image classification model using the transfer learning technique. This technique extracts features from a database that will have similar images being classified in the same category, then retraining the model depending on the available data.  

2. Speech processing project 

If you have interacted with Apple’s Siri, Microsoft’s Cortana, Amazon Alexa, and Google Assistant, you may well have an idea of the applications of speech recognition. Speech recognition is an enabler of hands-free use of devices and, ultimately, a better user experience.  

A speech recognition program is a machine program built with the capacity to identify spoken words and convert them into text. This project involves deploying a simple speech recognition system. You could build a program for language instruction used in education, the voice assistant in customer service, transcription, or emotion recognition, as these are all applications of speech recognition. 

You may train the Convolutional neural networks (CNN), NLP (natural language processing) model, Hidden Markov Model, or N-grams model with your own data. 

3. Plant disease detection robot 

Using TensorFlow and robotics, this project involves developing a robot that roams in a greenhouse to detect crop diseases using object detection techniques. In this project, you will first pre-train the MobileNet SSD model using your training dataset, which contains about 2000 images. You’ll then use XML data to create a map of the greenhouse and upload plant health information onto a website. 

The robot is typically built with navigation and classification cameras, a marking mechanism, and a paint reservoir as it will be marking the diseased plants. The navigation camera enables the robot to roam about the greenhouse without damaging the plants or the soil. The classification camera is used for detecting and classifying crop diseases. The marking mechanism, using ink from the reservoir, will then mark the diseased plants. 

4. Sudoku solver model

Sudoku is a digital logic-based puzzle that involves placing numbers 1-9 in a grid such that each number appears only once. 

This project involves using TensorFlow to build a computer program that can solve any sudoku puzzle that it will come across. This program analyzes the grids in the Sudoku puzzle, discovers the mathematical rule that applies, and fills in the numbers in the grids. This robot uses the Raspberry pi3 model and a camera alongside TensorFlow’s image processing technique. The camera captures the image of the grid that needs to be solved. The image is then pre-processed into grids which are segmented into individual boxes. Each box is analyzed using neural networks to get a numerical figure represented by the box. 

5. WildEye 

Illegal wildlife trade such as poaching elephants for their tusks, rhinos for their horns, and deers for their antlers. This illegal trade diminishes endangered animal and plant species and also leads to the destruction of habitats.  

This TensorFlow project involves building a detector system that employs deep learning and the internet of things (IoT) to detect and send out a signal on illegal wildlife and plant trade. The WildEye system is deployed in certain wildlife-protected zones where it monitors the zone and collects data on animal species, population, activities, and tracks their movements.  

6. Music genre classification projects

This project involves building an automated system that classifies music under different genres, including hip-hop, jazz, blues, and rock. Classification basically involves feature extraction from audio files, processing them, and training a classification model. Such features that will be used as classifiers include sound, tone, acoustics, and frequency components. The TensorFlow framework is used for feature extraction, model building, model training, and model deployment online. 

7. Customer segmentation 

These days, businesses thrive on the personalization of customer experience. To achieve personalization, one of the techniques used is customer segmentation. Customer segmentation involves classifying customers according to their purchase history, interest, preference, age, gender, and more to better understand customer behavior and expectations. Organizations use customer databases in their possession with data drawn from their customer relationship management systems. A clustering algorithm is then trained that will analyze and process data to provide insights for decision-making during product development and marketing. 

Conclusion 

In the near future, data will be one of the most valuable and costliest resources a business will have. It is one thing to possess valuable data and quite another to crunch this data effectively to make the most out of it. Frameworks like TensorFlow were developed for this sole purpose. For beginners, working on a TensorFlow project not only enhance your skills and knowledge but also makes you a worthwhile asset for the organizations that you will work for. You can be sure that given the wide adoption of AI and ML, the demand for TensorFlow skills will continue to soar.