Author: Zen and the Art of Computer Programming
1 Introduction
Engaged in intelligent solution development, architecture design, product development, etc. Mainly responsible for the research and development and project management of intelligent business systems.
I have rich experience in product development, including software development experience in traditional IT industry, database design, business requirement analysis, project management, teamwork, etc. In Zhiyuan's work, we are warmly accepted by the business staff. We continue to learn new knowledge and improve our professional ability.
I have always insisted on doing meaningful things, solving practical problems, and striving to improve my ability level. I have been fond of programming since I was a child, and I like to study new technologies and apply them to the actual production environment. At the same time, I also like to discuss technology and share experience with my colleagues.
In my work, I hope that I can take into account both theoretical knowledge and practical ability, give full play to my advantages, and help companies and customers achieve better products.
2. Introduction to core work areas
AI (Artificial Intelligence) solution engineer. The role of an engineer involves multiple links such as product development, project management, algorithm development, data processing, and operation and maintenance management. These roles are independent of each other and require not only strong programming skills, but also relevant theoretical knowledge of computer science, mathematics, and statistics in order to be truly implemented. Here is my introduction to some of the core technologies involved in AI solutions engineering:
①Machine learning: A technology for prediction or classification based on historical data, which can be used in monitoring and early warning, decision support, recommendation systems, search engines, image recognition, natural language processing, etc. Machine learning algorithms generally include classification, clustering, regression, dimensionality reduction, association rules, neural networks, support vector machines, decision trees, etc.
②Deep learning: Deep learning technology refers to the technology of feature extraction, efficient classification and learning of raw data through multi-level neural networks. Deep learning has a wide range of applications in image, speech, text, video, audio and other fields.
③Reinforcement learning: Reinforcement learning is a machine learning method that attempts to improve the behavior of an agent through interaction with the environment. It is applicable to different fields such as autonomous driving, control optimization, medical diagnosis, etc. Reinforcement learning can solve complex decision-making problems and promote system improvement in an intelligent way.
④Natural language understanding: NLP refers to