TensorFlow and PyTorch 2021
TensorFlow vs PyTorch? In 2017 they were different. TensorFlow was really cryptic, it was almost like learning a new programming language, while PyTorch was very Pythonic. For today, a lot of differences disappeared between the two machine learning libraries. They are almost the same.
Machine Learning is gaining bigger and bigger popularity. The basic idea is that computers can learn from data, find patterns and improve their decision making and predictive accuracy. The evolution of Machine Learning is Deep Learning that uses artificial neural networks to make decisions without the help of humans. TensorFlow and PyTorch are two ML libraries helping developers and researches.
A short introduction into the world of Machine Learning
As you may know, Machine Learning (ML) is a branch of Artificial Intelligence (A.I.). Many AI solutions use M.L., e.g. the algorithms that recommend products based on our previous searches or movies based on your browsing history.
In contextual online advertising, algorithms evaluate online contents and recommend them for the relevant user.
Machine learning is also used for developing chatbots, vacuum cleaner and humanoid robots or even self-driving cars.
Deep Learning is an improved form of M.L. It uses a programmable neural network to make its own decisions, without any human help.
It is also used in medical image analyses and these algorithms provide better results than human doctors. These solutions work under the supervision of human specialists.
After that short introduction, let’s see these two frameworks!
TensorFlow and PyTorch
TensorFlow is an open-source library developed by Google. It is from the original Machine Learning software of the company. TensorFlow is used for ML applications and artificial neural networks.
PyTorch is an open-source machine learning library based on Torch. It was developed by the other tech giant, Facebook. It is also used for developing M.L. applications and natural language processing solutions.
TensorFlow has both high- and low-level API, while PyTorch has a lower level one. It is very Pythonic from the beginning, while TensorFlow was really cryptic at that time. It was like learning a new programming language.
To be honest, for today the two APIs are very similar to each other.
They are almost the same.
Then, which one to choose for my ML project?
Traditionally, PyTorch is very popular among python fanatics and researchers. TensorFlow is more scalable, so it is preferred in production.
For today, after the recent releases, they become more similar. So, when choosing between them, you can’t make anything wrong.
Sources and further readings:
PyTorch vs. TensorFlow: What You Need to Know, Updacity