As Machine Learning continues to strengthen its grasp on the industry and the world around us, there’s a new trend that’s emerging with it – the rise of TensorFlow. Developed by the Google Brain team, TensorFlow is one of the most popular ML and Deep Learning framework right now.
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TensorFlow is a Python-based open-source library designed for numerical computations and Machine Learning. It incorporates the choicest assortment of Machine Learning and Deep Learning algorithms and models.
TensorFlow eases the processes of data acquisition, model training, and serving predictions while also fine-tuning future results. It uses Python to create a convenient front-end API for building applications with it while executing those applications in high-performance C++.
Since TensorFlow expedites the incorporation of AI and ML features, including computer vision, voice recognition, NLP, etc., into applications, an increasing number of companies are adopting the framework for ML. The success stories of some of the big names in the industry like SnapChat, AirBnB, Dropbox, Airbus, and Uber in leveraging TensorFlow are driving others to follow their footsteps. TensorFlow is one of the top Python libraries for Machine Learning.
The rising popularity of TensorFlow is propelling Data Science enthusiasts to get handsy with the framework and building TensorFlow models for real-world applications.
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Most Interesting TensorFlow Projects
The illicit wildlife and plant trade market are estimated to be worth $70-213 billion a year. Not only do these illegal trading activities harm the balance of the ecosystem, but they also adversely affect the businesses and tourism of countries around the world. The WildEye project was created to keep wildlife trafficking and human-wildlife conflicts in check.
This TensorFlow-based project leverages the latest technologies in Deep Learning and the Internet of Things (IoT) to detect and send out an alarm each time any such illegal activity is detected. The WildEye system is deployed in various parts of the wildlife protected zones in Kenya to monitor and gather data on the species thriving there, their populations, their activities, and their whereabouts.
While this will paint a comprehensive picture of the wildlife and plant species there, the networked camera traps, capable of analyzing images on the edge of protected areas in near real-time is an effective tool in the fight against poaching.
2. Farmaid: Plant Disease Detection Robot
Yes, you heard that right! Farmaid is a TensorFlow-based ML Robot that can drive around autonomously within a greenhouse and identify the diseases of plants. The project drew inspiration from the work of plantvillage.psu.edu and iita.org, and the idea was to design an autonomous robot that can move around in a farm environment without damaging the plants or soil and identify diseased crops or plants using object detection technique.
In the conventional approach, human farmers have to identify and mark diseased plantation manually, which is both time-consuming and labour intensive. While there are phones that can help with this, they do not always have all the features for efficient detection. This is something that Farmaid can solve.
3. Meter Maid Monitor
John Naulty launched the Meter Maid Monitor at the TechCrunch Disrupt Hackathon in September 2016. Meter Maid Monitor combines TensorFlow image classification with a Raspberry Pi motion detection and speed-measuring. The goal was to create something that could help people avoid parking tickets.
According to John, with Meter Maid Monitor “one can park their car, knowing that a notification will arrive via text message notifying them of a passing Meter Maid.” The alert would commence the two-hour parking time limit allotted to them in the parking area. The Meter Maid Monitor uses Raspberry Pi with a camera module and OpenCV as a motion detector.
The camera monitors traffic and captures images after which it uploads them to AWS, where an EC2 instance running on TensorFlow performs image recognition. The system is trained to recognize Meter Maid vehicles, and whenever the image turns out to be a Meter Maid match, it sends a message via Twilio with a link to the image.
SIGHT is a pair of smart glasses for the blind that allows them to make sense of what’s going on around them. Powered by TensorFlow and Google Android Things), SIGHT has three core components – a Raspberry Pi 3 (backed by Android Things), a camera, and a button. When a blind person presses the button on the SIGHT device, it captures an image of the scene before them. This image is then analyzed using TensorFlow that detects the objects in the picture and assists the person about the surroundings through the SIGHT voice assistant.
5. Sudoku Solver Bot
For those who are unaware of what Sudoku is, it is a digital puzzle that computers can solve since they adhere to simple mathematical rules.
As the name suggests, the Sudoku Solver Bot can solve and fill Sudoku grids. The idea behind the creation of this bot was to build an autonomous system that can analyze Sudoku grids, figure out the missing pieces of the puzzle, and fill the grid.
The Sudoku Solver Bot’s hardware consists of Raspberry Pi 3 and a camera. The camera takes the photo of the grid to be solved. The image is then pre-processed using TensorFlow image processing. Each grid is segmented to extract individual boxes which are then analyzed via image recognition using a neural network.
By the end of the process, the bot delivers a numerical representation of the grid that can be used to fill the gaps. Now the Raspberry Pi comes into functioning – it controls the motors of the bot and helps it to fill the Sudoku grid.
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TensorFlow’s ease-of-use factor and seamless incorporation of AI and ML features make it suitable for experimenting with model building. While we’ve named only five TensorFlow-based projects, there are numerous other projects out there that are as exciting as these. Data Science enthusiasts around the world are actively contributing to creating such fantastic projects that can have a meaningful impact in a real-world scenario.
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Which should I prefer – TensorFlow or Keras?
TensorFlow is a high-level library while Keras is a python library that wraps lower level TensorFlow functionalities in simpler to use high-level APIs. So, if you want to focus on learning higher level APIs, Keras will serve you well. On the other hand, if you want to focus on learning the TensorFlow ecosystem and it's lower-level details then you should use TensorFlow directly. TensorFlow documentation is quite well written with lots of examples and the google engineers behind TensorFlow are very active on boards. TensorFlow also has a great community of contributors and has achieved a very high level of bug-free-ness.
What can I build with TensorFlow?
TensorFlow is an open-source library for Machine Intelligence. It is a very flexible library. You can use it for both research and production. You can build intelligent apps, games, and services. It can be run on a CPU or a GPU. The developers can focus on building and training one model to perform well on different kind of data. Some frameworks like Torch and Theano use TensorFlow as its backend. TensorFlow has a shorter learning curve and is easy to use. It has a lot of high-level APIs, so developers can build complex applications using simple programming commands.
How can I learn TensorFlow?
You can start by reading the documentation. TensorFlow is not as hard as it may seem at first. It is like learning a new language, you first learn to read, then you learn to write and at the end you learn to speak. So, start by reading documentation, then play with sample code and then start implementing the concepts on your own.