TensorFlow Lite provides a set of tools that enables on-device machine learning by allowing developers to run their trained models on mobile, embedded, and IoT devices and computers. It supports platforms such as embedded Linux, Android, iOS, and MCU.
Now, let’s have a detailed look at it.
What is TensorFlow?
TensorFlow is an open-source software library for AI and machine learning with deep neural networks. It was developed by Google Brain for internal use at Google and open-sourced in 2015. Today, it is used for both research and production at Google.
Tensorflow is an open-source library for Numerical Computation and Large-Scale Machine Learning. It is a combination of machine learning and deep learning models and algorithms.
The user can use machine learning and all of its products to improve the search engine, the translation image captioning or the recommendations to give you a concrete example.
In fact, Google users can experience a faster and more refined search with artificial intelligence. For example, If the user types a keyword in the search bar, Google provides a recommendation about what could be the next.
Companies Using TensorFlow
The Airbnb ingenious and data science team applies machine learning using tensorflow to classify the images and detect objects at scale helping to improve the guest experience.
GE Healthcare is using TensorFlow to train a neural network to identify specific anatomics during the brain MRI exam to help improve speed and reliability.
Paypal is using it as a flow to stay at the cutting edge of fraud detection using tensorflow deep trance for learning and generator modeling. They have been able to recognize complex fraud patterns to increase fraud decline accuracy while improving the experience of legitimate users through increased Precision in identification.
China Mobile is using tensorflow to improve their success rate of the network element cut overs channel while has created a deep Fist amusing tensorflow that can automatically predict the cut over time window, verify log operations, and detect Network anomalies. Moreover, this has already successfully supported the world’s largest relocation of hundreds of millions iot HSS.
1. Open-source Library
It is an open-source library that allows rapid and easier calculations in machine learning. Open-source library eases the switching of algorithms from one tool to another TensorFlow tool.
With the help of python, it provides the front-end API for the development of various machines and deep learning algorithms.
2. Easy to run
We can execute TensorFlow applications on various platforms such as Android, Cloud, IOS and various architectures such as CPUs and GPUs. This allows it to be executed on various embedded platforms.
TensorFlow has its own designed hardware to train the neural models known as Cloud TPUs (TensorFlow Processing unit).
3. Fast Debugging
It allows you to reflect each node, i.e., operation individually concerning its evaluation. Tensor Board works with the graph to visualize its working using its dashboard. It provides computational graphing methods that support an easy to execute paradigm.
It works with multi-dimensional arrays with the help of a data structure tensor which represents the edges in the flow graph. Tensor identifies each structure using three criteria: rank, type, shape.
It provides room for prediction of stocks, products, etc with the help of training using the same models and different data sets. It also allows for synchronous & asynchronous learning techniques and data ingestion. Moreover, the graphical approach secures the distributed execution parallelism.
6. Easy Experimentation
TensorFlow transforms the raw data to the estimators-a form of data neural networks understand. Its feature columns enable the bridge between raw data and estimators to train the model. This adds the agility to the model for fast developmental insights.
TensorFlow provides a multiple level of abstraction by reducing the code length and cutting the development time. The user needs to focus on logic disregarding the proper way of providing input to functions. We can build and train models by using the high-level Kira’s API, which makes getting started with tensorflow and machine learning very easy.
TensorFlow provides the process of resolving complex topologies with the support of Keras API and data input pipelines. Keras provides easy prototyping and suits best for object-oriented neural networks. If you need more flexibility, then Kera execution allows for immediate iteration and intuitive debugging. When you enable eager execution, you will be executing tensorflow kernels immediately rather than constructing graphs that will be executed later.
Whether you are an expert or beginner, TensorFlow is an end-to-end platform, which helps you build and deploy ML models easily. Apart from the information provided here, if you want to know more, get in touch with us.