Document/Knowledge.md

ONNX Runtime inference can support models from deep learning frameworks like PyTorch and TensorFlow/Keras, as well as traditional machine learning libraries such as scikit-learn, LightGBM, and XGBoost, enabling faster customer experiences and lower costs; ONNX Runtime is compatible with various hardware, drivers, and operating systems, providing optimal performance through graph optimization and transformation, and utilizing hardware accelerators when applicable.

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Study Major Knowledge by reading papers and implementing them to build a strong foundation and following the curriculum of major knowledge.

Preparation for AI Programming includes preparing data structures and algorithms, algorithms + coding tests, Python, etc.

Build a portfolio by creating possible projects (source: https://f-lab.kr/insight/becoming-an-ai-developer).

Learning through online courses, graduate programs, and practical experience where graduate programs are suitable for those who want deeper learning, practical experience helps apply theoretical knowledge to real projects, platforms like GitHub and Kaggle offer opportunities to participate in various AI projects.

Through networking and community participation, participate in AI-related conferences, workshops, and hackathons, keep up with the latest technology trends, and it's good to interact with industry experts.

Programming languages like Python.

Mathematics and statistics such as Linear Algebra, Probability Theory, Calculus, etc.

Machine learning (ML) and deep learning, learn AI-related algorithms and apply them to real projects, specialized knowledge in specific fields such as Natural Language Processing (NLP) and Computer Vision.

Since continuous advancement is ongoing, it is important to continuously learn the latest technology trends.