As the popularity and popularity of machine learning grows in various industries, so has the creativity, initiative, and study of data science and deep learning. Deep learning is actually one of the subfields of machine learning, the accuracy of which, due to the very high volume of data, is far better than machine learning and even simulates the decision-making power of the human mind. In this tutorial, we are going to introduce you to 3 of the best deep learning frameworks in 2021.


The best deep learning frameworks in 2021

1- TensorFlow framework; The best framework for developing DL models

Tensorflow, Google's open source platform, is the most popular tool for machine learning and deep learning. This framework is a JavaScript-based platform that utilizes a variety of tools and resources to facilitate learning and develop deep machine learning and learning (ML / DL) models.

Although the TensorFlow kernel tool allows you to build and develop models in the browser, you can use the Lite version to develop models on mobile and embedded systems. So if you want to develop ML and DL models that work in large, practical environments, the Tensor Flow framework will do just that.

Although it is possible to use the Tensor Flow framework in programming languages ​​such as JavaScript, # C ++, C, Java, etc., the best language to work with this framework is the Python programming language. It should also be noted that while DL / ML models can be run on very powerful computing systems, TensorFlow can even run models on Android and iOS operating systems.

Among the strengths of this framework, we can mention the integration functions, including input graphs, SQL tables and images. As a weakness of this framework, however, we must mention the difficult and almost impossible process of debugging in it.

2- PyTorch framework

This deep learning framework is developed by Facebook and is based on the Torch Library. The main purpose of this framework is to speed up the entire prototype manufacturing process in the research phase until the model is established. Thanks to this framework, you can easily use common debugging tools such as PDB and PyCharm.

This framework is more than anything for learning, building and developing small projects and prototypes. This framework is widely used to develop deep learning applications such as natural language processing and machine vision.

3- Keras framework

Another open source framework is Keras Deep Learning. The strong point of this excellent framework is its speed because this framework has the ability to parallelize work with data. Therefore, this framework, while working with a large amount of data, greatly speeds up the learning of models. Since this framework is written using Python, its use is very simple and extensible.

The Keras Deep Learning Framework is a great choice for those who are new to the field, providing you with a quick experience of deep neural networks. Thanks to this framework, you can also write legible and accurate code.

Other deep learning frameworks include Cafee, Sonnet, MXNet, Gluon, DL4J, ONNX, and more.