Three best deep learning frameworks
deep learning frameworks

Deep learning is one of the fields in the industry that has many applications. In this tutorial, we will introduce you to 3 of the best deep learning workshops in 2021.

As machine learning’s popularity and popularity grow in various industries, so has the creativity, initiative, and study of data science and deep learning. Deep learning is one of the sub-fields 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 human mind’s decision-making power. In this tutorial, we will 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 framework

Tensorflow, Google’s open-source platform, is the most popular tool for machine learning and deep learning. The 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 mobile and embedded systems models. So if you want to build ML and DL models that work in large and practical environments, the Tensor Flow framework will do just that for you.

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 this framework’s strengths, we can mention the function integration functions, including input graphs, SQL tables, and images. However, as a weakness of this framework, we must note the complex and almost impossible process of debugging in it.

2- PyTorch framework

PyTorch framework

This deep learning framework is developed by Facebook and is based on the Torch Library. This framework’s primary purpose 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 standard 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 create deep learning applications such as natural language processing and machine vision.

3- Keras framework

Keras framework

Another open-source framework is Keras Deep Learning. The strong point of this excellent framework is its speed because it can synchronize work with data. Therefore, while working with a large amount of data, this framework dramatically speeds up models’ learning. Since this framework is written using Python, its use is very simple and extensible.

The Keras Deep Learning Framework is an excellent choice for those 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.

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