Github is one of the most popular platforms for developers. It hosts numerous projects, so if you want to find out the most successful projects in a specific domain, you should head there. Knowing what’s popular and what’s not ensures that you’re updated with the latest developments in your field.
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That’s why we’ve discussed the top AI projects in Github today. Most of the projects we discuss here require you to be familiar with the basics of artificial intelligence and machine learning. Let’s get started:
Top AI Projects in Github
1. TensorFlow
TensorFlow tops the list of AI projects in Github for multiple reasons. First, it’s the most popular machine learning framework; second, it is open-source. Lastly, it has tons of features that simplify working on AI projects. It has an extensive collection of libraries and tools with community resources that allow researchers to build ML applications with ease.
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TensorFlow has some of the most prominent ML frameworks and API which make it a must-have for any AI professional. You can build machine learning models quickly by using high-level APIs such as Keras. Its APIs make multiple tasks easy, including debugging and model iterating. Another advantage of using TensorFlow is its accessibility.
You can use TensorFlow to train and deploy models in the browser, on-premise, in the cloud, or on your device through any language you use. It facilitates experimentation for research purposes, so if it’s an excellent choice for academics too.
TensorFlow has more than 1,46,000 stars and 82,000 forks on Github. It is a product of Google Brain Team, which was under Google’s Machine Intelligence Research group. TensorFlow has C++ and Python APIs as well. All in all, it’s a must-have for anyone interested in using AI technology.
2. scikit-learn
scikit-learn is built on SciPy and is a Python module for ML. It entered the market in the form of a Google Summer of Code project in 2007, and its creator is David Cournapeau. It allows you to use Python for machine learning and is undoubtedly an excellent tool for any Python and AI developer.
Apart from SciPy, it is built on matplotlib and NumPy too. You can reuse it in multiple contexts, and its accessibility enhances its suitability further. One of the best advantages of scikit-learn is its collection of tools for predictive data analysis. However, you can also use scikit-learn for regression, classification, dimensionality reduction, clustering, preprocessing, model selection, and other AI applications. It allows you to use prominent algorithms, including K-means clustering, and random forest with ease and efficiency.
If you’re a Python developer, you must learn scikit-learn to work on ML projects properly. It has over 41,000 stars, and so it’s also among the top ML projects in Github.
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3. Handson-ml
If you want to do some reading on machine learning and AI, then this is the right project for you. It has many Jupyter notebooks on the basics of deep learning and machine learning in Python.
Jupyter notebooks are a product of jupyter.org and are digital books with interactive code and visuals. You can try out the examples present in the notebook directly while using them, which makes them a great learning tool for developers.
All the problems this project shares are related to TensorFlow and scikit-learn, so make sure you’re familiar with them before you start working on these problems. While solving these exercises might seem useful if you’re interested in getting a more personalized and detailed learning experience, you should consider getting an AI course. A course will provide you with dedicated support and make sure that you study every topic effectively.
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4. Qix
Qix is another collection of resources that you can use to study machine learning and artificial intelligence. It shares translated Chinese papers on ML, covering different topics. However, most of the content present in it is still in Chinese. It has more than 13,000 stars on Github and is among the top-rated projects there.
5. PredictionIO
PredictionIO is an open-source ML framework. It enables deploying algorithms, event collection, their evaluation, and querying predictive results through REST APIs. It is based on other prevalent open source technologies such as HBase, Elasticsearch, Hadoop, and Spark. It creates predictive engines for machine learning projects. You can systematically evaluate multiple engine versions, respond in real-time to dynamic queries, enhance ML modeling, and simplify the management of data infrastructure with PredictionIO.
It is a product of Apache, which has produced many other popular open-source products. You can install it as a part of a machine learning stack along with MLlib, Apache Spark, Elasticsearch, and Akka HTTP. Learning about PredictionIO will help you in using multiple analysis tools in AI projects with more effectiveness. It has more than 12,400 stars on its Github page, so you can see how popular it is among the top ML projects in Github.
Check out: Python Projects in Github
6. LightGBM
LightGBM is a distributed, fast, and high-performance framework for gradient boosting (MART, GBRT, GBDT, GBT). It is based on decision tree algorithms, and you can use it for classification, ranking, and similar machine learning applications.
It is a product of Microsoft and offers the following advantages to the developer:
- It can handle large-scale data with ease
- It supports GPU and parallels learning
- It has very high accuracy and consumers very low memory
- It can train with high efficiency and speed
Due to these advantages, many competition-winning teams have used LightGBM in the past. Its developers ran comparison experiments too and claimed that LightGBM could beat other boosting frameworks on all counts (accuracy and efficiency).
There are multiple unofficial repositories related to LightGBM present on its Github page, which can help you in enhancing its capabilities further. It has a thriving community of users and developers on Slack and other platforms where you can discuss any issues you encounter while using LIghtGBM. It surely deserves a place in your arsenal.
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Learn More About AI
Artificial intelligence is one of the broadest topics to learn. Working on projects and getting familiar with popular tools, frameworks, and libraries will help you in becoming a proper AI professional.
If you want to learn more about artificial intelligence, machine learning, and related topics, we recommend starting with our blog. There, you’ll find plenty of valuable resources on these topics ranging from AI project ideas to interview questions for AI developers.
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How do you implement AI projects?
Here are 3 essential actionable steps that help you implement AI projects effectively: 1. Decide what needs to be done: The most important thing is to analyze the needed improvement and what tasks should be done by humans, and what tasks should be done by the machine. 2. Prepare the data: Most of the AI based projects are based on data. So, in this step, you will have to prepare the necessary data needed for the AI to analyze. 3. Implementing the projects: Prepare the needed data and share it with the best Artificial Intelligence development company.
How can we improve artificial intelligence?
AI is getting better each year. However, one of the biggest roadblocks is what will happen when a computer becomes smarter than a human. Researchers have already started to think about this problem and the best way to solve it is to build a computer with a “nano chip” that controls the ability of the computer to access the Internet. The nano chip will be built with the sole purpose of protecting the computer from viewing or downloading information that might harm it. The computer with the nano chip might not become smarter, but it will know when to stop researching.
What is AI project cycle?
The AI project cycle consists of three distinct phases, each of which requires its own methodology and approach. The first phase is the definition of the problem, the research and analysis of the data to get tangible information about the problem. The second phase is the development of the solution based on the current state of the problem and the data collected from it. The last phase of the cycle is the implementation and testing of the solution on the market. The last phase is extremely important and requires constant feedback from all stakeholders, taking into account the changing market conditions.
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