Machine Learning Programming Essentials
The Important Deep Learning Libraries to Look out for in 2021 and Why TensorFlow Should be Your Choice
The field of deep learning is very exciting. From reinforcement learning applications to image classification and sound generation, there is a wild amount of application areas. While we work on these exciting projects, we often want to “outsource” the tedious work such as writing to model algorithms to deep learning frameworks.
There are several deep learning frameworks designed & backed by big tech, universities, and researchers. But, maintaining a deep learning framework is not an easy task. There are many deprecated deep learning frameworks even though they are backed by big tech firms like Microsoft. Therefore, finding a deep learning framework that is still in active development is crucial for the future of your project. In this post, we will learn about the most up-to-date deep learning frameworks in 2021 and understand why you should choose TensorFlow.
TensorFlow is an open-source machine learning platform with a particular focus on neural networks, developed by the Google Brain team. Despite initially being used for internal purposes, Google released the library under the Apache License 2.0 in November 2015, which made it an open-source library. Although the use cases of TensorFlow are not limited to machine learning applications, machine learning is the field where we see TensorFlow’s strength.
Especially with TensorFlow 2.0, Google has improved the user-friendliness of TensorFlow APIs significantly. Besides, the TensorFlow team announced that they don’t intend to introduce any other significant changes. Therefore, the skills you gain in TensorFlow 2.0 will remain relevant for a long time.
There are more than two dozen deep learning libraries developed by tech giants, tech foundations, and academic institutions that are available to the public. While each framework has its advantage in a particular sub-discipline of deep learning, excelling in TensorFlow with Keras API is the soundest option. The main reason for choosing TensorFlow over other deep learning frameworks is its popularity. On the other hand, this statement does not indicate that the other frameworks are better -yet, less popular- than TensorFlow. Especially with the introduction of version 2.0, TensorFlow strengthened its power by addressing the issues raised by the deep learning community. Today TensorFlow may be seen as the most popular deep learning framework, which is very powerful & easy-to-use and has excellent community support. Figure X shows the workflow of TensorFlow applications.
Installing and importing TensorFlow is fairly easy:
But, let’s also talk about the other deep learning frameworks:
Starting from the 80s, researchers, universities, and enterprises started several initiatives to build powerful deep learning libraries and frameworks. Below you can find a list of these tools:
Although the total number of deep learning frameworks is more than twenty, many of them are not currently maintained by their designers. Therefore, we can only talk about a handful of active and reliable deep learning frameworks. In this post, we will talk about four deep learning frameworks in addition to TensorFlow, which are:
Let’s cover them in briefly:
Keras is an open-source neural network library written in Python which can run on top of TensorFlow, Microsoft Cognitive Toolkit, Theano, R, and PlaidML. François Chollet, a Google engineer, designed Keras to enable fast experimentation with neural networks. It is very user-friendly, modular, and extensible. Keras also takes pride in being simple, flexible, and powerful. Due to these features, Keras is viewed as the go-to deep learning library by newcomers.
Keras should be regarded as a complementary option to TensorFlow rather than a rival library since it relies on the existing deep learning frameworks. In 2017, Google’s TensorFlow team agreed to support Keras in its core library. With TensorFlow 2.0, the Keras API has become more streamlined and integrated, which makes it easier to create neural networks.
To install and import Keras:
Keras Official Website: www.keras.io
PyTorch is an open-source neural network library primarily developed and maintained by Facebook’s AI Research Lab (FAIR) and initially released in October 2016. FAIR built PyTorch on top of the Torch library, another open-source machine learning library, a scientific computing framework, and a scripting language based on the Lua programming language, initially designed by Ronan Collobert, Samy Bengio, and Johnny Mariéthoz.
Since PyTorch is developed by Facebook and offers an easy-to-use interface, its popularity has gained momentum in recent years, particularly in academia. PyTorch is the main competitor of TensorFlow. Prior to TensorFlow 2.0, despite the issues on the ease-of-use of its APIs, TensorFlow has kept its popularity due to its community support, production performance, and additional use-case solutions. Besides, the latest improvements with TensorFlow 2.0 has introduced remedies to the shortcomings of TensorFlow 1.x. Therefore, TensorFlow will most likely keep its place despite the rising popularity of PyTorch.
To install and import PyTorch:
PyTorch Official Website: www.pytorch.org
MXNet is used and supported by Amazon, Intel, Baidu, Microsoft, Wolfram Research, Carnegie Mellon, MIT, the University of Washington. Although several respected institutions and tech companies support the MXNet project, the community support of MXNet is limited. Therefore, it remains less popular compared to TensorFlow, Keras, and PyTorch.
To install and import Apache MXNet:
MXNet Official Website: mxnet.apache.org
Microsoft released CNTK as its open-source deep learning framework in January 2016. CNTK, also called The Microsoft Cognitive Toolkit, has support in popular programming languages such as Python, C++, C#, and Java. Microsoft utilized the use of CNTK in its popular application and products such as Skype, Xbox, and Cortana, particularly for voice, handwriting, and image recognition. However, as of January 2019, Microsoft stopped releasing new updates to the Microsoft Cognitive Toolkit. Therefore, CNTK is considered deprecated.
To install and import CNTK (it is unfortunately much more complicated and the below version is for Google Colab notebooks):
Microsoft Cognitive Toolkit Official Website: www.cntk.ai
As of 2020, it is safe to state that the real competition is taking place between TensorFlow and PyTorch. Even though PyTorch is doing a fantastic job, I would still put my bet on TensorFlow for several reasons:
- TensorFlow is More Mature
- TensorFlow Supports More Programming Languages
- TensorFlow is Still More Popular in Job Market
- Community Support of TensorFlow is Uncanny
- TensorFlow Offers Many Supporting Technologies
- TensorFlow 2.0 is Very Easy to Use
On the other hand, Keras is not really a rival to TensorFlow, more like a complementary framework and I strongly recommend you to take advantage of Keras resources as well as its API.
Now it is a bit outdated, but in 2018, Jeff Hale developed a beautiful power ranking for the deep learning frameworks on the market. He weighs the mentions found in the online job listings, the relevant articles and the blog posts, and on GitHub. His results also support the evaluations above:
based on his weighted average scores of deep learning frameworks, we can draw a power score bar chart like this:
If you would like to have access to the codes of my other tutorial posts on Google Colab, and have early access to my latest content, consider subscribing to the mailing list:✉️
If you are interested in deep learning, also check out the guide to my content on artificial intelligence:
Since you are reading this article, I am sure that we share similar interests and are/will be in similar industries. So let’s connect via Linkedin! Please do not hesitate to send a contact request! Orhan G. Yalçın — Linkedin