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Wednesday, May 29, 2024

Top 10 Open Source Machine Learning Projects to Watch Out for in 2022

Python, together with data science & Machine Learning, is disrupting the software development industry. For becoming a data scientist or machine learning engineer or Data Engineer, it is essential to understand and implement some of these open-source machine learning projects.  You can practice and use those projects especially [Data Engineering Projects in ProjectPro]. It’s because open-source projects usually promote a free exchange of ideas and implementation with the development community. Programmers and developers should contribute to open-source projects to understand how to write clean code, gain better technological understanding, improves coding skills, etc. This article will discuss the top 10 open-source machine learning projects that everyone should watch out for in 2022.

Top 10 Open-source Machine Learning Projects in 2022

  1. Kornia: It is an open-source machine learning project that works as a Vision library for PyTorch. This latest library caters to solutions to some generic Computer Vision (CV) challenges. It can manage and compute complex vision-related functionality in machine learning projects.


  1. DeOldify: It is an interesting open-source ML project that contains a deep learning model. The development team has trained this to convert grayscale images, old clips, and photos to high-quality colorization with proper intensity. This open-source ML project leads to outstanding results. The main objective of this project is to restore the image in colored form and render new life to old pictures and film footage.


  1. Keras: Keras is a popular, open-source machine learning & deep learning API. It was developed by François Chollet under Google’s project and is a Python front-end with a high level of abstraction. Since it is beginner-friendly, most machine learning enthusiasts start using it in the early stages of ML project development.


  1. Real-time voice cloning project: This machine learning project takes 5 seconds to mimic anyone’s voice. Then it can generate arbitrary speech with that cloned voice or random text. This creepy open-source project uses deep learning models. Voice-recognition biometric security systems may get obsolete because of this project.


  1. MLJAR, an Automated ML tool for Humans: MLJAR is another excellent open-source ML platform that helps construct prototype instances and ML deployment services. This project tries to find the best model, using different searches on various algorithms and performing hyperparameters tuning. It uses the concept of AutoML to generate reports at the end of its processing. It can efficiently generate impressive quick results by conducting multiple estimations through cloud-based processing.  


  1. TensorFlow: It is an open-source ML library and project developed by the Google Brain team 2012. TensorFlow helps ML engineers in generating & training ML models. This project utilizes dataflow programmers in dealing with large-scale supervised & unsupervised learning. It also employs numerical computations. APIs and programming modules save developers from rewriting ML logic. Developers mostly use Python to implement this library in their projects.


  1. NeuralTalk2: It is another Google’s Brain Team project that generates image captioning from images and videos. It understands the picture & video and prepares the caption using Multimodal Recurrent Neural Network built on top of Python and NumPy. It is the second version of NeuralTalk that is much better & faster than the original one. This project also helps in educational purposes. Implementing it in your project requires GPU to execute it.


  1. U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation (U-GAT-IT) is another open-source ML project. It generates an unsupervised image to image translation that brings a new attention module to convert a person’s picture to their anime avatar. It can also create avatars of animals. This ML project can translate and render images by giving them a holistic change to make them look like a cartoon or avatar. It uses a novel unsupervised image-to-image translation mechanism for images requiring large-shape variation. In other words, we can say that this ML project is every anime lover’s favorite.


  1. DeepMind Lab: It is a highly customizable three-dimensional gaming platform that helps AI and ML engineers to perform research and development on machine learning that requires extensive vector graphics and other gaming intelligence. DeepMind Lab gets created from the “ioquake3” Game Engine. Moreover, this open-source project leverages tools like bspc & q3map2 for creating maps. It comes bundled with navigational tasks and a challenging puzzle that pivots on deep reinforcement learning. This project also caters to a flexible and nifty API that helps engineers design innovative gaming tasks, situations, and AI-generated designs that get iterated as per requirement. Apart from games and visuals, this project also helps train and research AI/ML models.


  1. Goldfinch: The abbreviation goes something like GOogLe image-search Dataset for FINe grained CHallenges (GOLDFINCH); a unique machine learning project is a dataset that helps solve various object recognition challenges through fine-grained data. It comprises a collection of numerous real-life objects like dogs, butterflies, aircraft, birds, etc. It also utilizes Flickr search URLs and Google image searches for more images and things to make an image dataset. Categories like dogs include various dynamic learning annotations. Companies like Google and other large and small firms use this open-source ML project to explore & solve fine-grained recognition problems.

It’s time to wrap up by saying that integrating these open source projects in your new data science projects will help you get a clean code and better understand how machine learning leverages datasets. Lots of these projects work as a project module that you can import as libraries, APIs, etc. Some also help bring ease, efficiency and save time when implemented in large projects.

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