Reasons why tensorflow is important.

The status quo is slowly and steadily shifting towards machine learning and deep learning. The algorithms which were mere words on a research paper about a decade ago are now at the forefront and have become the harbingers of the new “industrial revolution.” It is estimated that within the foreseeable future, data would be the costliest commodity one could buy. Hence, to stay relevant, it is imperative to have a TensorFlow certification.

Both on paper and in practice, all the deep learning and machine learning algorithms look amazing. However, for those of you who aim to venture beyond, TensorFlow is calling. TensorFlow is an open-source library with the ability to be used with multiple languages and provide all of the critical machine and deep learning algorithms with minimal code lines. Even if you take a look at all the job postings in this domain, you will find that TensorFlow is a must-have for any organization. That is why we have carefully curated this list of the top 10 TensorFlow projects, which should help you improve your skills and bag your dream job.

However, let us first understand the reason why TensorFlow is so essential in the context of machine learning and deep learning.

Why is TensorFlow important?

There are many reasons why TensorFlow is so dominant and hence, important in the paradigm of machine learning and deep learning. We believe one would be able to clearly understand the reason once he/she has looked at all the apparent benefits that this framework has over others in the market. You will find the same listed down below:

  1. It is an open-source library that is created by Google. This means, all the code which is written for this library is available to everyone for free under the MIT open-source license. Since it is open-source, it would be easy for developers to work with and around it, which means the TensorFlow community would be both passionate and huge. It also has the backing of Google, so the quality of the code is assured.
  2. TensorFlow has a very easy to understand and use syntax, which reduces the complexity of an already very complex program.
  3. With the TensorFlow version 2.XX, it now comes loaded with Keras Library. Keras allows developers to build highly sophisticated and complex models with just a few lines of code.
  4. TensorFlow is mainly written in C++, which means the code is very high performant. So, one would not feel the library slowing his/her progress down.

Top 10 TensorFlow projects for beginners

We have listed all the top TensorFlow projects down below:

  1. WildEYE:
    The illegal trafficking business is always booming. It is estimated that the illicit trade markets for both flora and fauna to be about 70 to 213 billion US dollars. Not only does this trafficking harm the indigenous genome pool, but it also makes life for the species being trafficked miserable. That is where this WildEYE project comes into the picture. Tapping into the robust and real-time object detection and recognition that come baked into TensorFlow, one could monitor for all such activities and let out an alarm whenever detected.
  2. Plant Disease Detector:
    With the help of TensorFlow and some robotics, one would be able to create a robot that would roam around in farmlands and report if and when it detects any crop threatening disease. If you are stepping foot into this domain for the first time, you could stop after you’re done with the model. And, once you are comfortable, you could scale it up and build the final robot.
  3. Meter Monitor:
    Have you ever been fined because you have exceeded the parking meter’s 2-hour limit? If your answer is yes, then this project would prove to be life-changing. In this project, with the help of Raspberry Pi and TensorFlow, you would build a notifier that would alarm you when the duration of the parking meter is about to finish.
  4. Smart Glasses:
    There are many variations of intelligent glasses available, but these are for the greater good. We would be using glasses with a camera, which would help those who have lost their vision to navigate this world better.
  5. Sudoku Solver:
    Sudoku is one of those games which presents immense satisfaction once it is solved and can frustrate you till the time it is unsolved. With the help of TensorFlow, you would be able to create a computer program that would be able to solve each and every sudoku puzzle which it would face.
  6. DeepSpeech:
    This project taps into the innate natural language processing capability of the TensorFlow framework. You would be using the TensorFlow library to create a text-to-speech and speech-to-text converter. You could also couple it with Raspberry Pi or Arduino to add more functionality.
  7. Object Recognition:
    We have dabbled with a subset of object recognition throughout this list; however, in this project, we would try to encompass them all. The goal of this project is to create a computer program that is able to recognize over a thousand objects.
  8. Image Converter:
    This is not your average image convertor. The aim of this project is to create a computer program that changes the style of any image without losing its defining features. We would be using CNNs and GANs.
  9. Human Activity Recognition:
    With the help of the LSTM model available in the TensorFlow library, you would be able to create a project which would be able to classify human motion (into categories such as running, jumping, sitting, crouching, and lying prone).
  10. Self-driving Cars:
    To be honest, you would need some finesse with TensorFlow to be able to pull off this project. The aim of the project is not to produce a fully functional self-driving car. However, if you are able to create a decent object detection model alongside one which would be able to measure distances in real-time, then you should count this project to be a success.

Working on a TensorFlow project will really boost your understanding of TensorFlow and how it works in real-life workplaces. In case you feel that you lack the necessary knowledge, you should opt for a TensorFlow certification and then bolster the acquired knowledge by selecting a proper TensorFlow project.

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