TensorFlow Cheat Sheet: Why TensorFlow, Function & Tools,

TensorFlow Cheat Sheet: Why TensorFlow, Function & Tools,

In this tutorial, we'll learnTensorFlow Cheat Sheet: Why TensorFlow, Function & Tools

Train your models with TensorFlow

Every tech enthusiast wants to master the complex discipline of Machine Learning. Acquiring and training datasets to allow a computer to learn patterns and make decisions accordingly can be overwhelming sometimes if you don’t know an easy way around. 

Google came out with a solution and called it TensorFlow. It is an open-source machine learning framework used to tackle and implement some tricky large-scale machine learning and neural networking models to make the job of predicting future results easier. A part of 

ML models that use multi-layer neural networks are called deep learning. It was developed to boost Google’s deep neural network research and can now be seen in the advanced Google search suggestions. The search engine giant with the largest set of data in the world needed some efficient way to scale up to massive models and algorithms.

TensorFlow was launched in 2017 and the current version stands at 2.2. TL has undergone several changes since it was first offered to the public. Some of the changes include added support for deep learning in computer graphics and discontinuation of support for Python 2.

How it Works…

TensorFlow provides an easy-to-work-with Python frontend API to get along with the framework while the core is written in C++ to get the high-level performance. Python is an easy-to-learn and work-with language and has good support for various kinds of libraries to make development faster and convenient. 

It runs on a graph framework, thus making it cross-platform. It can be used from CPUs and GPUs to mobile systems.

The terminology gives a hint of its working

  • Tensor means an array or a matrix containing some data sets. So, you can make a flowchart of how data flows in a graph. 
  • The graph is a widely used data structure employed in various fields of computer science and is often used to handle complex data sets. It has a series of nodes that are connected through edges. 

The nodes describe a series of computation that needs to be performed while the edges are the multidimensional dataset on which the operations need to be performed. 

The graph was picked deliberately as it has many advantages that give the tool its abilities – like being able to run on different platforms, and easily deployable.

Google has its own custom TensorFlow Processing Unit (TPU) specifically designed to render the Tensor models that provide further acceleration to the computation.

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