The use of deep neural architectures instead of the classic machine learning algorithms to train bigger models that can mimic the human brain while solving complex problems that involve heavy computing is known as deep learning. In this article, I will take you through the deep learning frameworks in Python that you need to learn to solve complex problems using deep learning.
Deep Learning Frameworks in Python
When we start to learn machine learning, we use the algorithms provided by the scikit-learn library in Python. Just like scikit-learn for machine learning, we have a few frameworks for deep learning that you should learn to implement deep neural architectures to solve a complex problem. So here are the best Python deep learning frameworks you should learn:
- Keras
- TensorFlow
- PyTorch
Now let’s go through all the three deep learning frameworks in Python mentioned above one by one to understand what these frameworks offer you for solving complex problems with deep learning.
Keras:
Keras is one of the most widely used deep learning libraries among the top 5 winning teams of Kaggle competitions. Using Keras makes it easier to run experiments, lets you try more ways to solve a problem than just winning contests. And one of the best features of Keras is that it is faster in terms of high-performance computing. You can also export the deep neural architectures created using Keras to run them directly in web apps, iOS, Android and embedded devices as well.
TensorFlow:
TensorFlow is like the scikit-learn for deep learning. The Keras framework that I just introduced to you in the above section is also built on top of TensorFlow. It helps you build and train machine learning and deep learning models using high-level libraries like Keras with faster execution. Most companies involved in heavy calculations use TensorFlow in their applications. Some of the popular companies using TensorFlow are:
- Airbnb
- DeepMind
- Intel
- Nvidia
- AMD
- Lenovo
- Uber
PyTorch:
PyTorch is an amazing deep learning framework in Python. It is easy to use compared to Keras and TensorFlow, and also enables the fast, flexible and efficient performance of deep neural architectures. It also provides good support for deploying deep learning models in iOS and Android apps. Some of the biggest names among companies and organizations that use PyTorch in their applications are:
- Salesforce
- Stanford University
- Udacity
Summary
PyTorch, Keras and TensorFlow are therefore the best deep learning frameworks in Python. If you have never used any of these frameworks, I would recommend that you learn Keras and TensorFlow together as they are both used together most of the time. After learning Keras and TensorFlow, it will be easier for you to learn PyTorch. Hope you liked this article on deep learning frameworks in Python. Please feel free to ask your valuable questions in the comments section below.
