Top 8 Exciting Features of TensorFlow to Know
Welcome to TechVidvan TensorFlow Tutorial series. In this article, we will learn Features of TensorFlow that make it worth learning.
In the emerging era of advancement, data plays an important role; where it collects the data, analyzes it and makes predictions providing a more user-friendly environment. With such technology, it trains the systems using machine-learning to provide rules using given data and output. Implementing machine learning is a complex stream concerning the creation of real-time models. Hence the framework- TensorFlow is used for collecting datasets, training systems through models and providing the results based on it.
TensorFlow is an open-source framework created by Google. It executes machine learning and neural networks with the help of python. It builds an environment of networks to experiment with the algorithms of machine learning and visualizes it using flow graphs. The graphs represent the progression of all nodes where nodes are the operations in the model.
Features of TensorFlow
Let us learn some exciting Tensorflow features:
1. Open-source Library
It is an open-source library that allows rapid and easier calculations in machine learning. It 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 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 and asynchronous learning techniques and data ingestion. 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. TensorFlow feature columns allow 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 defined 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. A user can choose the model apt according to the system’s requirement.
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.
TensorFlow eases the mechanism of machine learning with the assistance of such characteristics. It allows the user to create and manipulate the system to create different types of real-time models.
So, finally we have seen top Features of Tensorflow in this article. TensorFlow paves the way for the users to experiment with systems providing a platform that can be plotted and used on various systems. It allows users to test the samples with enhanced performance, robustness, and flexibility. It is developing to build a more advanced platform that provides optimization techniques for models.
Hope you liked the article. Do let us know in the comment section if you know any other tensorflow feature, we would be glad to add it.