How to start with AI open source projects ?

 Learning AI programming can be a challenging but rewarding endeavor. Here are a few steps you can take to get started:

• Start with the basics: Before diving into AI programming, it's important to have a strong foundation in computer science and programming concepts. Make sure you're comfortable with basic programming constructs like loops, variables, and functions in a programming language like Python or Java.
• Learn the fundamentals of machine learning: Machine learning is the most common type of AI, and it's a good place to start learning. There are many online resources and tutorials that can teach you the basics of supervised and unsupervised learning, as well as more advanced concepts like deep learning.
• Practice with small projects: Start building small projects that allow you to apply what you've learned in a practical context. For example, you can start with simple tasks like linear regression or KNN classification, and then move on to more advanced projects like image or speech recognition.
• Join a community of AI developers: Join online communities and forums where you can interact with other AI developers, ask questions, and share your own knowledge. There are many resources like StackOverflow, Kaggle, Coursera, Udemy, etc.
• Get hands-on experience: As you progress, you should try to work on real-world projects and get hands-on experience with different AI tools and frameworks like TensorFlow, PyTorch, Scikit-Learn, etc.
• Keep learning: AI is a rapidly evolving field, and it's important to keep learning and staying up-to-date with the latest developments and trends. Attend conferences, read research papers and blogs, and take courses to continue to expand your knowledge and skills.

Note: If you're completely new to programming, then you may want to start by learning the basics of programming first, before diving into AI-specific topics.

There are many open-source projects related to AI in Python, here are a few popular ones:

• TensorFlow: An open-source machine learning library developed by Google. TensorFlow is widely used for a variety of tasks, including image and speech recognition, natural language processing, and neural machine translation.
• PyTorch: An open-source machine learning library developed by Facebook. PyTorch is similar to TensorFlow in many ways, but it is considered to be more user-friendly and is often used for research and development.
• Scikit-Learn: A popular machine learning library for Python that provides a wide range of tools for data analysis and modeling. It is built on top of other popular libraries such as NumPy and matplotlib.
• Keras: A high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It was developed with a focus on enabling fast experimentation.
• OpenCV: An open-source computer vision library that provides a wide range of tools for image and video processing. It can be used for tasks such as object detection, image recognition, and video analysis.
• Natural Language Toolkit (NLTK): A Python library for natural language processing that provides tools for tasks such as tokenization, stemming, and part-of-speech tagging.
• Gensim: An open-source library for unsupervised topic modeling and natural language processing, built on top of NumPy and SciPy.
• spaCy: An open-source library for advanced natural language processing in Python. It is designed specifically for production use, and it is fast and efficient.
• DeepLearning4j : Open-source, distributed deep learning library written for Java and Scala, compatible with other JVM languages.

These are just a few examples of open-source projects in AI that are available in Python. There are many others out there, and new ones are being developed all the time. It's a good idea to explore different options and find the ones that work best for your specific use case.

Yes, there are several open-source AI projects available in Java as well:

• Deeplearning4j: An open-source deep learning library for Java and Scala. It is designed for developers and researchers to build, test and deploy deep learning applications. It has a wide range of functionality for building deep neural networks, including support for feedforward, convolutional and recurrent architectures.
• Java-ML: An open-source machine learning library for Java. It provides a wide range of tools for data mining and machine learning, including classification, regression, clustering, and feature selection.
• Encog: An open-source machine learning library for Java and .NET. It provides a wide range of tools for training neural networks and other machine learning models, and it is widely used for tasks such as image and speech recognition.
• Weka: A collection of machine learning algorithms for data mining tasks. It is written in Java and contains tools for data pre-processing, classification, regression, clustering, and association rules.
• MLlib: A machine learning library for the Apache Spark ecosystem. It provides a wide range of tools for distributed machine learning, and it is written in Scala (which runs on the Java Virtual Machine).
• Smile: A machine learning library for Java and other JVM languages. It provides a wide range of tools for data analysis and modeling, including support for classification, regression, clustering, and feature selection.
• Deep Java Library(DJL): A Java deep learning library developed by AWS. It can use deep learning framework such as PyTorch, TensorFlow, ONNX and MXNet.

These are just a few examples of open-source projects for AI available in Java. Keep in mind that there may be some overlap between Java and Python AI libraries, since some libraries may have a Java API or support for Java.