What Is So Good About Tensorflow
“TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications.” — TensorFlow Home Site
If you are a fan of so called cutting edge technology, artificial inteligent or even better, that deep learning thingy. You might want to check TensorFlow package that have been widely used in digital industry especially in data related fields.
First of all, it is easy
TensorFlow offers multiple levels of abstraction, which means it gives you flexibility to choose the right one for your needs. Build and train models by using the high-level Keras API, which makes new learner getting started with TensorFlow and machine learning easy.
Even more, If you need more flexibility, eager execution allows for immediate iteration and intuitive debugging. For large ML training tasks, you might want to use the Distribution Strategy API for distributed training on different hardware configurations that requires no change in model definition.
Second, It fits everywhere
You hear it right, unlike any other machine learning library. TersorFlow model can be deployed into various application platform, from desktop to website application to mobile device or even small device like raspberry pi.
- Tensorflow for machine learning on desktop. Tensorflow for desktop application, first of all lets put google collaboration aside to discuss this one. Tensorflow mainly is a library for Python and C family language. Which are widely used as data science tools a long with python jupyter notebook to analyst or build a model to make certain prediction cases.
- TensorFlow Lite for mobile and embedded ML. It is a TensorFlow lightweight solution that can be used for mobile and embedded devices. It is fast since it enables on-device machine learning inference with low latency. It also supports hardware acceleration with the Android Neural Networks API. It is not even stop there, the future releases of TensorFlow Lite will bring more built-in operators, performance improvements, and will support more models to increase the developer’s experience of deploying machine learning services within mobile devices.
TensorFlow Hub for reusable machine learning
A library which is used extensively to reuse existing machine learning models in various open source model repository. Thus this enable us to perform transfer learning by reusing parts of machine learning models.
When it comes to visualization of the training process, TensorFlow takes the lead. Visualization tools helps the developer track the training process and debug in a more convenient way. TenforFlow’s visualization library is called TensorBoard. TensorBoard currently leads the vizualization tools in case of supporting feature for observing training behavior from a model.
TensorFlow offers various use cases in various digital platform. If you are looking for a deep learning tools or some one who is getting started machine learning field, you might want to give TensorFlow a try