Artificial Intelligence is an approach, which brings machines closer to acting like humans. Going by the current trend, AI is touted as a tool that can facilitate the Area 4.0 revolution within organizations of all sizes and in all industrial sectors. AI application services are being adopted on an increasing scale. Or technology lovers to keep abreast of this quickly changing market, especially the open source AI projects, to implement successfully AI driven projects.
These successes in an accelerated condition often result in a situation where more research and financial resources are directed to the development of technology as quickly as possible. But doing research with AI that is changing really fast can be quite difficult. To speed up the application development process and facilitate better and more functional uses of the projects, the developers utilize AI open-source projects to create greater technologies based on deep learning.
List Of 7 Best AI Open Source Projects
Here is our list of the top source projects on GitHub that can be used by beginners. Since the AI source code writers released all their code made for such projects under permissive open-source licenses, you can modify and contribute to these open-source AI projects in any way that you consider suitable.
TensorFlow
TensorFlow is the most popular AI open-source deep learning project. First of all, it was installed for machine learning and deep neural networks research purposes by the Google Brain Team which is part of the Machine Intelligence research group(a kind of Open source intelligence). TensorFlow is considered one of the top open-source AI tools and frameworks for creating machine learning (ML) and deep learning (DL) applications.
Like other platforms, it has encountered opposition from alternative machine learning projects like PyTorch and Keras which are again open source. Conversely, it has seen its liking and has today become the leading AI open-source tool. Now there are a range of workflows with user-friendly, high-level APIs that a user with no prior knowledge of machine learning can use, and a professional can develop machine learning models in different languages.
Keras
Keras is a high-level framework for building neural networks that work on top of TensorFlow, CNTK, and Theano. Consider the situation where you need to select a deep learning framework that has prototyping enabled, supports both convolutional as well as recurrent nets, and works well on CPUs and GPUs. Well, that concludes the question. As a result, this is the library that fixes open-source AI projects.
Unlike others, this AI open-source project does not do operations that merely consist of the low-level aspects of the programming. Contrary to this, it uses libraries from related deep learning frameworks of Tensorflow and Theano to be the backends carrying out low-level computations such as tensor products, convolutions, and a myriad of others.
PyTorch
The best open-source project for the deep learning framework is PyTorch. This framework is in Python additionally the C++ backend API runs on top of a C++ backend API. Python Torch was initially designed as a platform switch from the Lua Torch framework targeting research implementations only. Nowadays, the PyTorch project itself represents an impressive community and includes projects, tools, models, and libraries built by academic and industrial researchers, application developers, and deep learning experts.
In contrast with other famous deep learning frameworks, such as TensorFlow, PyTorch exploits dynamic computing, which allows one to create a lot of complicated architectures. PyTorch allows the programmer to use standard and well-known Python syntaxes, which are easier to read. In addition, by exploiting Python’s inbuilt abilities to perform asynchronous execution, PyTorch helps to enhance the optimization of AI models.
Similar to this: Top 10 Open Source Intelligence Examples
Theano
Theano is an open-source AI project developed by the MILA group of the University of Montreal in Montreal Quebec province, Canada. It is a Python library that provides a user-friendly programming interface for NumPy or SciPy to calculate mathematically on multi-dimensional arrays. Through Theano it is possible to use GPUs when computing gradients, and it can create symbolic graphs automatically.
Theano was developed in order to realize high-level deep learning algorithms and is now commonly used in academic works and industry as the de facto standard of deep learning research and development. The remarkable performance of its computational power is where the problem lies; consumers complain about a difficult-to-grasp UI as well as non-explanatory error messages. Besides, Theano makes the process of finding out how to estimate gradients at differences in the place automatically and thus allows you to do gradient Descent as the model training way.
MXNet
MXNet (Apache MXNet) is an open-source deep learning framework, where deep neural networks can be used to define, train, and deploy them on various platforms such as cloud infrastructure and mobile devices. MXNet models are portable as they are so compact that they can fit in nearly any of the modern device’s memory. consequently, you will be able to put it immediately on small mobile devices or connected devices. MXNet is a palindrome; hence, the abbreviation stands for mix-network since it was crafted from diverse programming methodologies.
This framework supports different languages such as Python, R, C++, Julia, and Perl among others and is not forced to learn new languages for the sake of using other frameworks. It also allows developers to merge InfoPython is a fun and engaging introduction to the Python programming language for beginners. It also demarcates the developer the opportunity to deploy the neural network in eight different languages for inference purposes, in addition to the open-source research.
Fastai
Fastai was created with the purpose of bringing deep learning down to the common user. Keras handles the clarity and speed of development, while PyTorch is a customizable one. Fastai is known for its accessibility and robust nature that is easy to get started and quick to produce, yet is flexible and layered architecture.
Fast.ai presents a variety of APIs able to address all the needs of model development. The mid-API level allows the instrumental deep learning and data processing techniques for each application, while the high-level API helps the solution developers. Finally, the low-level APIs are available to support the evolution of large programs by providing a loop of optimized primitives that serve as both functional and object-oriented foundations upon which the applications are developed and customized.
Also Read: 1000 projects with Source Code and Documentation
OpenCV
OpenCV or Open Source Computer Vision Library is a means for computer vision applications which varies from CCTV analysis to picture analysis. The license model of the library is BSD-type license which means it can be used for both commercial and academic purposes. Contemplating C++, the OpenCV library has more than 2500 new algorithms and the classic ones. This technology is capable of recognizing faces in photos or videos, determining objects and extracting human mood and behavior in videos.
Surprisingly enough, this is just the initial step, where humans still work with films and pictures observing all the parts of them, including the storyline, extraction of 3D models, and much more. OpenCV library contains more than 500 functions over a wide range of visual tasks, including industrial product inspection, medical imaging, security, user interface, camera calibration, stereo vision, robotics, and more.
Wrap-Up
Those ones mentioned previously are a few of the leading AI open-source projects and libraries for newcomers to learn DNN techniques through hands-on experience. Both the novices and the experienced can, in turn, continue to develop and further contribute to the projects for the increasing open-source AI followers.
FAQs
You can participate through the open-source projects, code and model sharing as well as through collaboration with other developers and researchers. Co-creation which adds to the general knowledge and promotes group work helps further the discipline as an entity. Machine learning and AI are the spheres of active development.
There are various ways to search for open-source projects to put in your effort on. You can use GitHub and GitLab for that, where you can search for projects according to their programming language, topic or keywords. Also you can participate in online clubs and networks of your interests and developers launch projects, to get contributions.
Participation in AI initiatives is an interesting and successful activity. Start by soaking up the knowledge about these popular AI libraries and frameworks; TensorFlow, PyTorch, scikit-learn, etc. You can contribute by fixing bugs, renaming but not really adding features, improving documentation, or even creating tutorials and instructional materials.
Yes, there are a large number of open-source AI projects available out there. Through such platforms, AI algorithms, tools, and frameworks are made available allowing developers and researchers in the AI community to team up and create new and different concepts.
