TensorFlow is an open source system applied for large scale machine learning processes from Google. TensorFlow was originally developed by the Google Brain team for its own products, but later it was released under the Apache 2.0 open source license.
- Why TensorFlow?
The big deal about TensorFlow is that it picks the right language. TensorFlow is written in C++ and optimized for speed, it allows you to code in python which is easy to read and write. Python can be used to wrangle data, access the most powerful open source scientific computing tools, and build a web server to demonstrate any work. Secondly, TensorFlow operates easily with multiple GPUs. It is comparatively complex to work with multiple devices to achieve model parallelism. But TensorFlow makes it easier to spin up session for running code on various devices, without having to restart and exit your program. It’s compile time is also great and it has gone over the top by creating a powerful set of visualization for both network topology and performance.
TensorFlow gives the ability to easily train on one machine and to go distributed training on hundreds, even thousands of machines.
- Applications built using TensorFlow:
TensorFlow is mainly used for: Classification, Prediction, Understanding, Perception, Discovering, and Creation. The main uses of TensorFlow are for:
- Voice/Sound Recognition
The most well-known uses of TensorFlow are Sound based applications. With the proper data feed, neural networks are capable of understanding audio signals.
- Image Recognition
TensorFlow object recognition algorithms classify and identify arbitrary objects within larger images. This is usually used in engineering applications to identify shapes for modeling purposes, i.e. 3D space constructed from 2D images and by social networks for photo tagging.
- Video Detection
TensorFlow neural networks also work on video data. This is mainly used in Motion Detection, Security, Real-Time Threat Detection in Gaming, Airports and UX/UI fields.
- Text-Based Applications
Further popular uses of TensorFlow are, text-based applications such as sentiment analysis (CRM, Social Media), Fraud Detection (Insurance, Finance) and Threat Detection (Social Media, Government)
- Time Series
TensorFlow Time Series algorithms are used for analyzing time series data in order to extract meaningful statistics. They allow forecasting non-specific time periods in addition to generate alternative versions of the time series.
Some of the applications built using TensorFlow are:
- SmartReply – to automatically generate email responses
- Inception Image Classification Model
- Massively Multitask Networks for Drug Discovery
- On-Device Computer Vision for OCR
- Bringing TensorFlow to the enterprise
Enterprises with big data need a machine learning system with ease of expression for lots of machine language ideas and algorithms along with scalability to run experiments quickly. Businesses also need the system to be reproducible to make it easy to share and reproduce research. Also, a machine learning system should have production readiness capability to develop real products. Deep learning has become central to the machine learning implementation process. TensorFlow is already a popular system for deep machine learning proven by Google on many of its widely recognized products such as Google search, Google Photos, and Google Translate. Artificial intelligence (AI) has become quite smart with the advanced open source software. The main advantages that enterprises can have with TensorFlow are mentioned below.
TensorFlow is a highly supple system which gives multiple versions of the same model and can be served simultaneously. Its architecture is highly modular, which means one can use some parts individually or can use all the parts together. Such flexibility facilitates non-automatic migration to new versions and for testing experimental models.
TensorFlow has made it possible to play around an idea on the laptop without having any other hardware support. It runs on GPUs, desktops, CPUs, mobile computing platforms, and servers. It serves as a true portability feature.
3) Research and Production:
With TensorFlow rewriting of codes is not required and the industrial researchers can apply their ideas to products faster. The academic researchers can allocate codes directly with better reproducibility, helping to carry out research and production processes faster.
4) Auto Differentiation:
TensorFlow manages derivative computing processes automatically. It provides the user with the capability to visualize the graph extension when derivative of one value is computed by another. Its differentiation capabilities also benefit the gradient-based machine learning algorithms.
By assigning the compute element of the TensorFlow graph to different devices, it makes use of the most available hardware with its advanced support for threads, asynchronous computation, and queues. It also facilitates the language options to execute the computational graph. It can be said that machine learning (ML) serves as a key ingredient when it comes to improving the efficiency of various existing technologies.
- Use Cases of TensorFlow in different domains:
- Crop/Plant Disease Prediction
- Automated Crop Management (Differentiation between lettuce and weed)
- Predictive Weather Analytics (Earlier weather prediction to harvest crop)
- Recognize quality of food
- Automatic analyzing medical images such as MRIs, CT scans, X-rays
- Personal Assistant which can help Blind people or physically challenged people
With the release of TensorFlow, innovation in artificial intelligence will dramatically increase. We can even expect to see an increase in the machine learning research. It assures firmness, making it easier to pick up new features without worrying about breaking the existing code. The combination of ease of development, ease of deployment, a great interface, the backing by Google, and all available open source, it will push more of the community towards implementing with TensorFlow.