Python is traditionally very popular among developers for web development, scripting, process automation, and general applications. Yet, in recent times, Python programming language has become the go-to language for Machine Learning.
Python language has several libraries for Machine learning and Deep learning development. Many of which have become very popular.
What is TensorFlow? Why is TensorFlow the most preferred library for Machine learning? How popular is Tensorflow? What are some of the use cases?
What is a Software Library?
They typically include configuration data, documentation, message templates, subroutines, classes, values or type specifications, etc.
If you have read my previous article about the python frameworks, you may have a bit of confusion now. How is a library different from a framework?
The key difference is “Inversion Of Control (IOC)”. If your application calls for a library, you are in control. It calls your application.
If you need a functionality for your code you can call it in a library. Python Library- TensorFlow.
There are several great libraries in Python for machine learning and deep learning programming. Libraries like Keras, Theanos, TensorFlow, and Scikit-Learn have made machine learning development relatively easy.
With these libraries, it is easy to gather data, train models, serve predictions and refine future results.
What is TensorFlow?
TensorFlow is an open source machine learning library developed by Google. With this library, it is possible for high-performance numerical computation.
Google uses machine learning in all of its products to improve the search engine, translation, image captioning, recommendations, etc.
Google wanted to use these deep neural networks to improve its services. Together with a team of researches and developers, they built an Artificial Intelligence Model called Tensorflow.
Why is TensorFlow Popular?
It has a very great feature in it called, Tensorboard. This feature gives developers a visual representation of complex computations – graphs. With Tensorboard it is easier to debug the program and scale and deploy. Tensorflow runs both on CPU and GPU.
You can easily visualize each & every part of the graph which is not an option while using Numpy or SciKit. In the pre-Tensorflow age, there were some good ML libraries with good functionalities and methods.
Tensorflow attracts the largest popularity on GitHub compare to the other deep learning framework.
Use Cases of TensorFlow
PlantMD is an app that lets you detect diseases in plants using TensorFlow. After annotating thousands of cassava plant images, identifying and classifying diseases they trained a machine learning model using TensorFlow.
Safety and quality have always plagued the food industry. Artificial Intelligence (AI) enabled technology has a place in many restaurants today.
One interesting development is a food detection app that is currently in development.
A renowned Japanese food processing company, Kewpie Corporation, uses AI enabled TensorFlow machine learning. It has the capacity to detect various anomalies in food.
Banking & Finance
Companies like Mastercard are using ML for their ‘Decision Intelligence’ projects. It helps them discover patterns from historical shopping and spending habits of cardholders to detecting fraudulent activities.
In many cases, Tensorflow plays an important role in developing key machine learning models.
Pharma & Healthcare
Machine learning has the capability to predict healthcare outcomes.
Machine learning models build with TensorFlow also enables personalization of patient treatment and better management of hospital resources.
Iowa State’s technology helps analyze the visual data gathered from stationary cameras and cameras mounted on snow plows. NTT DOCOMO, Japan’s largest mobile service provider, launched a demand forecasting service for taxi operators. The service collects real-time people density from mobile phones and runs data analytics with a deep learning model on TensorFlow.
As TensorFlow is an open source library, we will see many more innovative use cases soon. This will influence one another and contribute to Machine Learning technology.