Artificial Intelligence and Machine Learning are bringing forth the Fourth Industrial Revolution. Businesses of all shapes and sizes across all industries are embracing these disruptive technologies to design innovative solutions catering to the demands of their target customers.
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Consequently, there’s a massive demand for talented professionals who’re well-versed in the nuances of AI and ML. In fact, companies are ready to pay top dollar to deserving candidates with the right skill set.
In light of the growing demand for AI and ML skills, it helps if you have a few real-world projects under your belt. When you work on projects, it shows potential employers that you have the drive and knowledge to get handsy with these technologies.
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If you’re looking for inspiring open-source Machine Learning projects, you’ve stumbled upon the right place!
Open-source Machine Learning Projects
GitHub open-source Machine Learning projects
DeOldify is a deep learning model designed to colorize and restore old images. You can colorize old photos and film footage with DeOldify that does a fantastic job of instilling life in them! It has been upgraded to deliver more detailed and realistic re-touches to grayscale images. Plus, the results show considerably less blue bias with minimal glitches.
2. Facial recognition
This application boasts of being the “world’s simplest facial recognition API for Python and the command line.” It can recognize and manipulate faces from Python or the command line using dlib’s state-of-the-art face recognition software. This deep learning model claims to have a 99.38% accuracy rate per the LFW benchmark. You can use the “face_recognition” command-line tool to perform face recognition on an image folder from the command line!
3. Voice cloning
This ML project is an implementation of Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis (SV2TTS). SV2TTS is a deep learning tool that can generate a numerical representation of a voice from any audio clip and train a text-to-speech model to generalize to new voices. This application can clone any voice in 5 seconds and produce arbitrary speech, all in real-time!
NeuralTalk2 is essentially an image capturing code written in Lua. It runs on GPU and requires Torch. NeuralTalk2 can caption images/videos with sentences by leveraging the Multimodal Recurrent Neural Network. This is an ideal tool for social media content creators – you can generate subtitles for your images/videos, and you can also use this model to create funny image/video content (ones with funny subtitles).
U-GAT-IT (Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation) is an ML project with a simple focus – to translate a person’s image into their anime avatar. This model can convert images requiring holistic changes and images requiring large shape variations by leveraging a novel unsupervised image-to-image translation technique. Needless to say, this is the perfect project for anime lovers!
Srez uses deep learning for image super-resolution – it can upscale 16×16 images four times their resolution to generate 64×64 photos. The results depict sharp and distinguished features that seem commendable enough compared to the training dataset. The underlying architecture includes a DCGAN that accepts the 16×16 image inputs to the generator network instead of multinomial gaussian distribution.
AVA is a framework that aims to deliver AI-powered and automated visual analytics. The first “A” in AVA has multiple connotations – it is an Alibaba framework that strives to become an “Automated, AI-driven solution that supports Augmented analytics.” AVA includes three packages, namely, CKB (storage space for empirical knowledge for visualization/charts), DataWizard (data processing library), and ChartAdvisor (the core component that suggests charts according to the dataset and analysis requirements).
Developed by NVIDIA’s Applied Deep Learning Research team, Megatron is a powerful transformer that can train voluminous language models to improve their performance as they scale up. It is an ongoing project that supports model-parallel, multi-node training of BERT & GPT2 via mixed precision.
Google open-source Machine Learning projects
Caliban is a tool designed for developing ML research workflows and notebooks in isolated and reproducible Docker environments. The best part – you don’t even need to learn the intricacies of Docker to use Caliban! With Caliban, you can build and run ML models on your machine and also ship the local code to the cloud. This tool is perfect for ML workflows on Pytorch, Tensorflow, and JAX.
Budou is an automatic line-breaking tool designed for CJK (Chinese, Japanese, and Korean) languages. It automatically translates CJK text into organized HTML code, resulting in beautiful typography. Budou fragments headings and sentences into multiple lines of meaningful chunks per the screen width of the browser.
This Google project is a statistics library that estimates an intervention’s causal effect on a time series model. The CausalImpact R package uses a structural Bayesian time-series to determine how the response metric evolves after the intervention if it hadn’t occurred in the first place. For instance, it is quite challenging to answer a question like “how many new clicks did a specific marketing campaign generate?” without using a randomized experiment. CausalImpact can help find answers to such questions.
12. DeepMind Lab
DeepMind Lab is a fully-customizable, first-person 3D game platform for the R&D of Artificial Intelligence and Machine Learning systems. It consists of a host of challenging puzzles and navigation tasks that are pivotal in deep reinforcement learning. DeepMind Lab has a neat and flexible API that allows you to create innovative task-designs and unique AI-designs that can be promptly iterated. Google’s DeepMind uses DeepMind Lab extensively to research and train AI/ML learning agents.
DeepVariant is an analysis pipeline that leverages a neural network to find genetic variants from next-generation DNA sequencing data. It uses the Nucleus library (containing Python and C++ code) to read and write data in common genomics file formats that seamlessly integrate with TensorFlow.
It is a TensorFlow-based research framework built for fast prototyping of reinforcement learning algorithms. Dopamine was designed as a small and intuitive codebase that enables users to experiment with radical ideas and speculative research. It has four core design principles:
- Easy experimentation
- Flexible development
- Compact and reliable implementation
- Reproducible results
Goldfinch is a dataset created for solving fine-grained recognition challenges. It includes a collection of different categories – bird, butterfly, dog, aircraft, and other categories along with relevant Flickr search URLs and Google image searches. The dog category includes numerous active learning annotations. Google uses Goldfinch to explore Computer Vision and Machine Learning techniques for fine-grained recognition problems.
Kubeflow is an ML toolkit exclusively designed for Kubernetes. It makes the deployment of machine learning (ML) workflows on Kubernetes portable and scalable. The main aim is to offer a simple way to deploy best-in-class OS for ML to multiple and varied infrastructures. You can run Kubeflow on any system or environment running Kubernetes.
This is a research project developed to explore how Machine Learning in creating music and art. This project’s primary focus is to build deep learning and reinforcement learning algorithms to produce songs, images, drawings, and other creative content. It is an attempt to create intelligent tools that enhance the abilities and potential of artists and musicians.
18. PyTorch Lightning
A lightweight PyTorch wrapper called PyTorch Lightning makes it easier to train and use complicated neural networks. It offers a high-level abstraction for typical machine learning tasks, allowing researchers and developers to concentrate more on model construction and less on boilerplate code. These open source machine learning projects offer sophisticated features, including gradient accumulation, mixed precision training, and automated handling of GPU and distributed training.
19. TensorFlow Extended (TFX)
TensorFlow Extended (TFX) is an end-to-end framework for delivering machine learning models at a scale suitable for production. It offers extensive resources and parts for creating scalable and dependable machine-learning pipelines. Data validation, pre-processing, model training, model analysis, and model serving are aspects of TFX. It helps businesses create reliable machine-learning systems that fit perfectly with their production settings. Machine learning open source projects for beginners will benefit from TFX’s scalability and reproducibility, as well as the ability to leverage existing machine learning models.
Another popular name in open source machine learning projects is Ludwig. It is a versatile and user-friendly open-source platform for developing and testing deep learning models without writing any code. It enables users to construct and test sophisticated machine-learning models using a straightforward YAML configuration file. Ludwig offers pre-built model architectures for typical applications like classification, regression, and sequence modeling and supports various data formats, including text, pictures, and time series.
21. Hugging Face Transformers
A well-known open source ML project package for natural language processing (NLP) activities is called Hugging Face Transformers. It is among the open source machine learning projects which offers a wide variety of pre-trained models for tasks like text categorization, named entity identification, and question answering. Additionally, the library provides simple-to-use APIs for optimizing these models on unique datasets. Hugging Face Transformers’ user-friendly interface and cutting-edge performance on several benchmarks have helped it become quite popular in the NLP field.
A popular open-source Python package for machine learning is called Scikit-learn. It offers a complete collection of tools for data preparation, feature selection, model training, and assessment. Numerous machine learning methods, including classification, regression, clustering, and dimensionality reduction, are supported by Scikit-learn. The library is a well-liked option for novice and seasoned practitioners due to its user-friendly API and thorough documentation. The machine learning open source projects for beginners is a great choice for quickly getting started with ML.
The popularity of XGBoost, an improved gradient boosting system, is due to its outstanding performance and scalability. Gradient boosting methods, which are often employed for regression and classification applications, are effectively implemented. Regularization, parallelization, and tree pruning are just a few of the cutting-edge features of XGBoost that help explain its exceptional prediction accuracy. The framework has won several machine-learning contests and is often used in commercial applications.
The open source ML projects control the whole machine learning lifecycle. It offers resources for tracking experiments, ensuring repeatability, packing models, and deploying them. Users of MLflow may manage model versions, track and compare trials, and deploy models to various platforms, such as cloud services and edge devices. Most of the open source ML projects seek to streamline the development and deployment of machine learning models, facilitating team collaboration and model iteration.
A low-code machine-learning package called PyCaret makes the entire machine-learning workflow simpler. It offers several pre-processing methods, feature selection strategies, and machine learning algorithms in a solitary unified user interface. Users may easily create and deploy machine learning models thanks to PyCaret’s automation of several repetitive operations and simplification of complicated procedures. It is made user-friendly for beginners and provides expert users with cutting-edge features.
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To wrap up, our final piece of advice would be to go through these projects and disintegrate them to understand the deeper nuances. This will help enrich your ML knowledge and teach you how ML technologies work differently in each project.
We hope that by diving deeper into these 17 open-source Machine Learning projects, you’ll find the inspiration to develop your own Machine Learning project!
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What are the problems that occur while using AI in healthcare?
The field of medicine demands transparency and the ability to describe clinical decisions. The use of deep learning and other AI models in the healthcare sector is highly beneficial but explaining the models is quite a task. There are also certain ethical considerations that AI clinical applications confront, such as privacy concerns for data used for AI model training and security concerns while the implementation of AI in the medical field.
How does AI make healthcare less expensive in terms of time and money?
AI algorithms in the field of medicine are less expensive than traditional approaches. People no longer need to undergo a slew of costly lab tests owing to the use of AI technology in the healthcare system. This can be seen in the potential of AI in identifying biomarkers capable of detecting certain disorders in the human body. The algorithms ensure that the majority of the manual labor in specifying these biomarkers may be automated. In this manner, they save time which is very crucial in this field.
How does using AI empower patients?
Wearable technology, such as smart watches, is already being used by a vast number of individuals worldwide to capture daily health data ranging from sleep patterns to heart rate. When this data is combined with machine learning, it may be possible to successfully inform individuals whether they are at risk of certain diseases long before the risk becomes severe or untreatable. Currently, mobile applications give granular-level patient profile information, which may help patients living with certain chronic conditions better manage their disease and thereby live healthier lives. With this approach, AI has the potential to empower us to make better health decisions for ourselves.