What is TensorFlow?
TensorFlow is a free end-to-end open software library open to all and used for dataflow and Neural Networks across a range of tasks in machine learning. It has comprehensive and easy to use tools, libraries that lets developers and researchers push the state-of-the-art in Machine Learning using Neural Networks and build and deploy Machine Learning models. It is ranked 5th amongst all open-source libraries and supported by backends written in C++ and has interfaces in Python, Java, Swift, and Android.
TensorFlow was first released for public in 2015 by the Google Brain team. At that time, when deep learning was just evolving then developers and researchers were occupied by just two libraries Caffe and Theano. In a short time, TensorFlow emerged as the most popular and mostly used library for deep learning and this is well explained by the Google trends chart
TensorFlow 2.x vs TensorFlow 1.0
TensorFlow is currently running on version 2.0 which officially came out in September 2019.
The key differences are as follows:
Ease of use: Many old libraries were removed, and some consolidated. For example, In TensorFlow 1.x many users may get confused due to the presence of so many models like Contrib, layers, Keras or estimators. TensorFlow Keras is used for model experimentation and TensorFlow Estimators are used for scaled serving, and both these APIs have user friendly interfaces.
Eager Execution: In TensorFlow 1.x. writing of code was divided into two parts first is building the computational graph and later creating a session to execute it. This was quite complex, especially in the big model it is very difficult to compute error if occurred in the beginning. While in tensorflow 2.x by default Eager Execution is implemented i.e. we don’t have to create a session to run the computational graph, we can get our output directly without creating any session.
Model Building and deploying made easy: TensorFlow Keras API, is one of the high level API of TensorFlow 2.0, in which the user has a greater flexibility for creating the models. One can define a model using Keras functional or sequential API. The TensorFlow Estimator API runs a model both on a local host or on a distributed multi-server environment without changing your model.
Simplified conceptual diagram for TensorFlow 2.0
Use of TensorFlow
Still we are in the early applications of Machine learning algorithms, but continue to evolve at a faster pace and introduce us to the more advanced algorithms and topics like Deep Learning. Deep learning uses algorithms known as Neural Networks, which are inspired by the biological nervous systems, such as the brain, to process information. It enables models to identify about every single data of what it represents and learn patterns.
The primary tool used by developers in the field of deep learning is TensorFlow. It is an open source artificial intelligence library, using data flow graphs to build models and also it allows developers to create multi layer Neural Networks in Machine Learning. TensorFlow is mostly used in the areas of: Classification, Perception, Discovering, Prediction and Creation.
1. Voice/Sound Recognition
One of the most well-known uses of TensorFlow is in the field of Sound based applications. Neural networks created by TensorFlow are capable of understanding audio signals. These can be:
Voice recognition – used in IoT, Automotive and UX/UI
Voice search – used in Telecoms, Handset Manufacturers
Sentiment Analysis – mostly used in CRM
Flaw Detection – used in Automotive and Aviation
If we talk about the most frequently used Voice/Sound Recognition with which most of us are familiar are Apple’s Siri, Google Assistant now for Android and Microsoft Cortana for Windows Phone all these are used by many of us on a regular basis.
Another common use case for Voice Recognition is of Language understanding. Sound based applications are used in CRM.If you want to read more on sound Recognition you may follow tutorial on this presented by Codelabs
2. Time Series
Time Series algorithms are used to extract meaningful statistics by analyzing time series data . They allow forecasting of non-specific time periods to generate alternative versions of the time series.
The most common use case of TensorFlow for Time Series is Recommendation according to your browsing history. You’ve probably heard of this use from Amazon, Google and Netflix where they analyze customer activity and compare it to the millions of other users to determine what the customer might like to purchase or watch. These recommendations are getting even smarter, for example, they offer you certain things as gifts (not for yourself) or TV shows that your family members might like according to your browsing history.
Here is a wonderful project on Time series Forecasting by TensorFlow give it a look once.
3. Video detection
TensorFlow neural networks also work on video data. This is used in Motion Detection, Real-Time Thread Detection in Gaming, Airports and UX/UI fields. Recently, Universities are working on Large scale Video Classification datasets like YouTube-8M aiming to accelerate research on large-scale video understanding, representation learning, noisy data modeling, transfer learning, and domain adaptation approaches for video.
TensorFlow is built on the principles of mathematical computational graphs which is similar to that of Theano and Torch.TensorFlow comes out to be better at solving complex problems due to additional support of distributed computing. Also deployment of TensorFlow models is already supported which makes it easier to use for industrial purposes, giving a fight to commercial libraries such as Deeplearning4j, H2O and Turi. APIs for Python, C++ and Matlab are of TensorFlow only. There’s also a recent surge in support for other languages such as Ruby and R. So, TensorFlow is trying to have universal language support.
So this was a bit of an overview on TensorFlow in which I tried to answer some basic questions like what TensorFlow is then I compared the older version of TensorFlow i.e. TensorFlow 1.0 with it’s new version which is TensorFlow 2.0 and followed by its uses. But it is just an overview and there is a lot more to explore.
Article by: Amit Shukla
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