Top 5 Deep Learning Projects to Participate in 2022

Currently, no other technology has proved itself to be as useful as deep learning and machine learning. More and more tech aspirants are moving towards the deep learning domain to utilize the benefits of the technology and deliver the best quality results. And what better way is there to learn about deep learning, other than indulging in deep learning projects. Here, we have enlisted the top deep learning projects that beginners can try out to boost their careers.

Tensor Flow: TensorFlow is a free and open-source software library for machine learning and artificial intelligence. It is a deep learning framework developed at Google Brain. It has a comprehensive, flexible ecosystem of tools, libraries, and community resources. It provides stable Python and C++ APIs, as well as non-guaranteed backward compatible API for other languages.

Keras: Keras is an open-source software library that provides a Python interface for artificial neural networks. It was developed with a focus on enabling fast experimentation. It is innovative as well as very easy to learn. It supports simple neural networks to the very large and complex neural network model.

OpenCV: Open Source Computer Vision Library is a powerful machine learning, and AI-based library used to develop and solve computer vision problems. Computer vision includes training a computer to understand and comprehend the visual world, identify elements, and respond to them using deep learning models.

Tesseract: Tesseract was originally developed at Hewlett-Packard Laboratories Bristold at Hewlett-Packard Co, Greeley Colorado. Tesseracts can be trained to recognize other languages. This project does not include a GUI application. Tesseract has Unicode support and can recognize more than 100 languages.

Faceswap: Faceswap is the leading free and Open Source multi-platform Deepfakes software. It was a huge step in AI development. Face Swapping with Classical and Deep learning Approach Objective of the project is to detect a face in the image but not for changing faces without consent or with the intent of hiding its use.