Are you studying Data Science? Have you used Tensorflow? If not, then you have to read this article to the end!
Because you will understand what TensorFlow is through this article! TensorFlow is a library developed by google and is one of the most popular and widely used libraries for developing and implementing Machine Learning and other algorithms that have a lot of mathematical operations to perform.
Google launched TensorFlow to introduce an ecosystem that provides a collection of workflows for developing and training models to implement Machine Learning in almost any application.
We all use TensorFlow so much without realizing that to use: Google Photos or Google voice, you use TensorFlow models indirectly; they work on a large group of Google hardware and are very strong in perceptual tasks.
TensorFlow has more than 150k GitHub stars and around 83.2k GitHub forks (Github Forks). The open-source TensorFlow repo on GitHub is a great resource.
This article will introduce you to TensorFlow, how to use it, and why to use it. Intrigued by the discussion? Come on, see the following explanation!
TensorFlow: A Closer Look
Tensorflow allows developers to create dataflow graphs, which describe how data moves through a graphical display or a series of processing nodes. Each node in the graph represents a mathematical operation, and each connection or end between these nodes is a multidimensional data array, also known as a tensor.
Tensorflow provides it with all for programmers using the Python programming language. Python is very easy to learn and use. If you are unfamiliar with it, you can read the basic Python programming language article. Nodes and Tensors in Tensorflow are Python objects. In addition, the Tensorflow application itself is an application built into Python.
Python does not perform calculations of mathematical operations. This library transformation is written using the high-performance C++ binary language in combining the two.
Tensorflow applications can run on almost any target device. Such as, among other things, local machines, clusters on cloud platforms, IoS and Android devices, CPU or GPU. Using the Google Cloud platform, you can run TensorFlow using a TPU (Tensorflow Processing Unit) for better acceleration. The model generated by Tensorflow can be applied to most of the devices that will be used for prediction results.
TensorFlow Release
Tensorflow 2.0, released in October 2019, changes the framework based on user input, making it easier to use (for example, using the Keras API), which is relatively simpler for the model training process and has better performance. Distributed training is made easy thanks to this new API. Additionally, this version supports Tensorflow Lite, making it possible to deploy models across a wider range of platforms.
However, with the various advantages offered in the Tensorflow 2.0 framework, the code written in the previous version needs to be rewritten, some only require minor changes, and some are significant changes to take full advantage of Tensorflow 2.0 features.
TensorFlow Banefits for Machine Learning Development
The biggest benefit that Tensorflow provides for machine learning development is an abstraction.
You don’t have to deal with the ins and outs of implementing an algorithm or looking for answers on the right way to connect the output of one function to another, so developers can focus on the logic of the application as a whole, let Tensorflow handle it from behind the scenes.
Tensorflow offers various conveniences for developers who need to debug and introspect into Tensorflow applications.
The “eager execution” mode allows you to evaluate and modify each chart operation separately and transparently, not constructing the entire chart as a single object and checking the whole thing simultaneously.
Visualizing the Tensorboard allows you to check directly and interactively. This tool is a web-based dashboard.
Tensorfollow earns a lot of profit commercially on Google. Google has driven the pace of its development behind the scenes and created many great offerings around Tensorflow that make it even easier to deploy and use.
The TPU mentioned above is used to speed up performance on the Google Cloud platform, an online hub for sharing models created using a framework in a browser or a mobile-friendly framework. And much more.
TensorFlow vs Competitor
TensorFlow competes with many other machine learning frameworks. PyTorch, CNTK, and MXNet are three main frameworks that address many of the same needs. Below I’ve noted where they stand out and fall short with TensorFlow.
In addition to being built in Python, PyTorch has many other similarities to TensorFlow: hardware-accelerated components under the hood, a highly interactive development model that allows for custom-made design work, and lots of useful features already included.
PyTorch is generally a better choice for fast development projects that need to be up and running quickly, but TensorFlow wins for larger projects and more complex workflows.
CNTK, Microsoft Cognitive Toolkit, like TensorFlow, uses graphical structures to describe data flows but focuses mostly on building deep learning neural networks.
CNTK handles many neural networks that work faster and has a wider set of APIs (Python, C++, C#, Java). But CNTK is currently not as easy to learn or implement as TensorFlow.
Apache MXNet, adopted by Amazon as the premiere deep learning framework on AWS, can scale almost linearly across multiple GPUs and machines.
It also supports various language APIs — Python, C++, Scala, R, JavaScript, Julia, Perl, and Go — although the native API isn’t quite as fun to work with as TensorFlow.
1. Pytorch
Besides being made using Python and having the same similarities with Tensorflow. Using hardware-accelerated prime mover components, the highly interactive development model allows the design process to go hand-in-hand with work and has many other features.
Pytorch is generally a better choice for rapid development, where it takes a short time to complete a project. On the other hand, TensorFlow excels for larger scale projects and more complex workflows.
2. CNTK
CNTK is a Microsoft Cognitive Toolkit, like Tensorflow, that uses graphical structures to describe data flows but focuses mostly on building deep-learning neural networks.
CNTK can handle many neural network tasks faster and has a wider set of APIs (Python, C++, C#, Java). However, CNTK is not as easy to learn or implement as Tensorflow.
3. Apache MXNet
Amazon adopted Apache MXNet as a deep learning framework on AWS, scalable linearly across multiple GPUs and machines. This framework can also support various APIs such as Python, C ++, Scala, R, JavaScript, Julia, Perl, and Go. However, the native API isn’t as fun to use as TensorFlow.