Look at some of the best open-source AI projects you might love to contribute to on GitHub to add value to your resume.
Artificial Intelligence is a technique for a machine to imitate human behavior. Today, AI is touted to be instrumental in enabling Industry 4.0 for organizations of all shapes and sizes across all industry verticals. The use of AI applications is continuously expanding, and tech enthusiasts must stay up with this fast-changing sector, especially with open-source AI projects, to deploy AI-driven projects successfully.
As a result of these quick breakthroughs, extensive research and financial resources are devoted to speeding up technology development. However, keeping up with the fast-paced breakthroughs in AI may be challenging. To help accelerate the application development process and enable more efficient and effective practical usage, developers rely on open-source AI projects to build superior deep learning-based solutions.
We’ve compiled a list of the best AI open-source projects for beginners on GitHub. Since they’re all released under permissive open-source licenses, you can contribute and alter them as you see fit.
TensorFlow is the leading open-source AI project for deep learning. The Google Brain Team inside Google’s Machine Intelligence research group initially created it for machine learning and deep neural networks research. TensorFlow is one of the top-rated tools for developing machine learning and deep learning applications. Professionals use it worldwide to design text, audio, and picture recognition algorithms. It has faced competition from alternative platforms like PyTorch and Keras, much like any other platform. However, it has maintained its popularity and established itself as a leader in the AI industry.
Today, it offers an array of workflows with intuitive, high-level APIs that allow novices and professionals to develop machine learning models in various languages. Models created using TensorFlow can be deployed on multiple platforms, including servers, the cloud, mobile, edge devices, browsers, and more. In other words, TensorFlow is a cross-platform framework, which means it works on a wide range of hardware, including GPUs and CPUs and mobile and embedded platforms. You can also run TensorFlow on Google’s proprietary TensorFlow Processing Unit (TPU) hardware to accelerate further the development of deep learning models.
You can use TensorFlow to train and execute deep neural networks for handwritten digit classification, visual recognition, word embeddings, recurrent neural networks, sequence-to-sequence models for machine translation, natural language processing, and PDE-based simulations.
Built by Facebook and released on GitHub in 2017, PyTorch is one of the best open-source AI projects. This framework is written in Python that runs on top of a C++ backend API. PyTorch began as a Python-based replacement for the Lua Torch framework, focusing only on research applications. The PyTorch ecosystem comprises projects, tools, models, and libraries created by a diverse community of academic and industrial researchers, application developers, and deep learning experts.
Unlike most prominent deep learning frameworks, such as TensorFlow, PyTorch employs dynamic computing, providing greater flexibility in creating complicated networks. PyTorch uses basic and well-known Python and has a better readable syntax, making it much easier to grasp. Also, by leveraging Python’s intrinsic capabilities for asynchronous execution, PyTorch improves the optimization of AI models. Its Distributed Data Parallelism feature allows you to grow projects by running models across numerous computers.
Serial libraries such as Torchvision (for computer vision), Torchtext (for natural language processing), and even Torchaudio (for sound processing) help make the PyTorch ecosystem work efficiently. PyTorch’s strength comes from its open-source nature since it is the product of innumerable contributions from machine learning developers and academics worldwide. PyTorch’s ability to construct DL/ML solutions is practically limitless as the community behind it increases.
Keras is a high-level neural network framework that operates on top of TensorFlow, CNTK, and Theano. Suppose you require a deep learning framework that allows quick prototyping, supports convolutional and recurrent networks, and operates well on CPUs and GPUs. In that case, this is the perfect library for carrying out open-source AI projects.
Unlike other independent alternatives, this open-source AI project does not deal with simple low-level operations. Instead, it uses libraries from related deep learning frameworks like Tensorflow or Theano as backend engines to do all low-level computations such as tensor products, convolutions, and many other things.
TensorFlow, Theano, and Keras feature ready-to-use interfaces that allow quick and easy access to the backends. There’s also no need to commit to a particular framework because you can quickly move back and forth between the many backends.
Keras also offers High-Level API, responsible for creating models, specifying layers, and configuring various models. In addition, this API helps build models with loss and optimizer functions and the training process using the fit function.
Detectron2 is the updated version of Detectron, an object detection library developed by Facebook AI in 2018. Powered by Caffe, Detectron was hard to install and use. This was primarily because, since 2018, several code modifications have combined Caffe2 and PyTorch into a single repository, making Detectron more challenging to use. As a result, Facebook released Dectortron2 after receiving some constructive input from the open-source community.
Detectron2 is a next-generation software system from Facebook AI Research that uses cutting-edge object identification algorithms. It offers several methods to implement complex algorithms for DensePose, panoptic feature pyramid networks, and various variations of FAIR’s pioneering Mask R-CNN model family. It enables object detection using boxes, instance segmentation masks, and human pose prediction, just like Detection. Detectron2 also supports semantic and panoptic segmentation, which blends semantic and instance segmentation.
Theano is an open-source AI project created by the MILA group at the University of Montreal in Montreal, Quebec, Canada. It is a Python library that aids in using NumPy or SciPy to perform mathematical operations on multi-dimensional arrays. Theano can leverage GPUs to speed up processing and automatically create representational graphs to compute gradients.
Theano was created to implement state-of-the-art deep learning algorithms and is now considered an industry standard for deep learning research and development. While its computational performance is remarkable, consumers complain about an inaccessible UI and unhelpful error messages. As a result, Theano is most commonly used in conjunction with more user-friendly wrappers like Keras, Lasagne (which provides convenience classes for creating deep learning models), and Blocks — three high-level frameworks for rapid prototyping and model testing. There are still several benefits that many data scientists find compelling enough to keep them using Theano, such as its simplicity and maturity.
Theano helps define, optimize, and evaluate several mathematical procedures. Moreover, Theano can automatically find out how to estimate gradients at various places automatically, allowing you to use gradient Descent for model training.
MXNet (Apache MXNet) is an open-source deep learning framework for defining, training, and deploying deep neural networks on various platforms, including cloud infrastructure and mobile devices. The models created using MXNet are compact enough to fit in minimal amounts of memory. As a result, you can quickly deploy it to mobile devices or connected equipment. MXNet stands for mix-network since it was created by merging diverse programming methodologies into a single framework. This framework supports various languages, including Python, R, C++, Julia, Perl, and many others, removing the need to learn new languages to use alternative frameworks. It also allows developers to mix imperative and symbolic programming models, offering low-level control and high-level APIs.
Similar to other frameworks like TensorFlow and PyTorch, MXNet supports multi-GPU and distributed training. It also allows developers to export a neural network for inference in up to eight languages, giving them more flexibility in machine learning research.
OpenCV, or the Open Source Computer Vision Library, is a powerful tool for computer vision applications, including video, CCTV, and picture analysis. Published under a BSD license, OpenCV is free for academic and commercial use.
Based on C++, the OpenCV library has over 2,500 state-of-the-art and classic algorithms. These algorithms can distinguish faces in images or movies, identify objects, and characterize human emotions and behavior in videos. Additionally, this AI open-source library allows films and photos to be examined in all their components, including the trail of item motions, the extraction of three-dimensional models from these objects, and various other uses.
Over 500 functions are included in the OpenCV library, covering a wide range of visual themes such as industrial product inspection, medical imaging, security, user interface, camera calibration, stereo vision, and robotics. In addition, as computer vision and machine learning are frequently intertwined, OpenCV also includes a comprehensive Machine Learning Library (MLL). This sub-library is primarily concerned with statistical pattern detection and clustering. This machine learning library is very effective for computer vision problems but can be used for any machine learning problem.
Fastai is a well-known open-source AI project for implementing deep learning and machine learning techniques. The library includes vision, text, tabular and time-series analysis APIs, and collaborative filtering. Fastai v2, released in August 2020, claims to be significantly faster and more adaptable when implementing deep learning frameworks.
Fastai was created to make deep learning more accessible to the general public. It combines Keras’ clarity and development speed with PyTorch’s customizability. Fastai is known for its accessibility, quick-to-produce, highly flexible nature, and layered architecture.
Fastai offers different levels of API that cater to various model-building needs. The mid-level API provides the essential deep learning and data-processing methods for each application, while the high-level API aims for solution developers. Finally, the low-level APIs offer a library of optimized primitives and functional and object-oriented foundations, allowing for the development and customization of the mid-level.
Built on top of Tensorflow, TFlearn is a modular and transparent deep-learning library. It was built to deliver a higher-level API to TensorFlow to make experimentation more accessible and faster while staying fully transparent and compatible. Most modern deep learning models, such as Convolutions, LSTM, BiRNN, BatchNorm, PReLU, Residual networks, and Generative networks, are presently supported by this high-level API.
TFlearn comes with complete transparency thanks to the TensorFlow work system. It allows non-specialists to work on developing AI open-source projects using a general-purpose, high-level language. It enables researchers to develop, benchmark, and compare their novel methods in a structured setting.
TFlearn also has a set of useful helper functions for training any TensorFlow graph, including support for multiple inputs, outputs, and optimizers. It also provides easy-to-understand and attractive graph visualization with weights, gradients, activations, and more information.
10. HuggingFace Transformers
HugginFace’s transformer libraries have been on the minds of every NLP (Natural Language Processing) practitioner. They provide user-friendly APIs for creating custom models from scratch or fine-tuning pre-trained models for transformer-based models. HugginFace Transformers offers general-purpose architectures — like BERT, GPT-2, XLM, DistilBert, XLNet, and more — for Natural Language Understanding (NLU) and Natural Language Generation (NLG), with over 32+ pre-trained models in 100+ languages.
The HugingFace Transformers open-source library no longer requires PyTorch to load models, train state-of-the-art (S.O.T.A.) models in three lines of code, and pre-process a dataset in less than ten lines. In other words, HuggingFace claims that their Transformers library made it simple for academics and engineers to employ S.O.T.A. models by removing the complexities of topologies, frameworks, and pipelines.