TensorFlow Explained
Introduction
TensorFlow is an open source software that is well-suited for large-scale machine learning. Open source means that the code is available for its users to inspect, modify, copy and use in almost any way they choose. Tensorflow supports a number of different computing including distributed computing. This means that you can train neural networks on big datasets in a small amount of time by splitting the computations across hundreds of servers. It can train a network with millions of parameters and billions of instances. This software was developed by the Google Brain team and it powers a lot of Google’s large-scale servers such as Google Cloud Speech, Google Photos and Google Search.
TensorFlow became open-sourced in 2015 and it was not the first open-source library for deep learning. In fact, most of the TensorFlow features already existed in different libraries. But, since it had many optimal features and of course the Google name behind it, it quickly made its way towards the top of the list. The software was designed to be flexible, scalable, clean design and great documentation.
Advantages
- The software runs on mobile devices including both iOS and android as well as Windows, Linux and macOS.
- Keras, a high-level API, has been built independently on top of Tensorflow
- The main Python API offers more flexibility (which unfortunately led to more complexity) to create a variety of computations including neural network architecture.
- Autodiff or Automatic Differentiating — Allows you to search for the parameters that minimize the cost function. Tensorflow automatically takes care of computing the gradients of the functions you define.
- TensorBoard — visualization tool that allows you to browse through the computation graph and more
- Best of all, it has a team of developers that work on constantly improving the software so it is up to date and can be taken advantage of to its full potential.
History
If a lot of data is present then the best way to handle it is through deep learning as opposed to machine learning algorithms. Google noticed this a few years ago which is why they developed an open source software called TensorFlow. This allows developers to work together to build this AI model.
Architecture
There are three steps to the process:
- Preprocessing the data
- Building the model
- Training and estimating the model
The inputs are tensors, also known as multi-dimensional arrays. Once you have the input, it flows through the system, performs a bunch of operations and then on the other end you have the output.
Components of TensorFlow
- Tensor: A vector/matrix that of n-dimensions that represents all types of data. This is the input.
- Graphs: gathers and describes all the series and computations done during the training.
Popularity of TensorFlow
This software has the best reputation because it was built for everyone to access and manipulate to suffice their own needs.
