As an academician of China Academy of Sciences and an expert in machine learning, Gong has made remarkable achievements in the field of artificial intelligence. He has been committed to applying machine learning technology to various fields and has made important contributions to the development of human society. This paper will introduce Gong's exploration road, from his academic experience, research direction to specific operation steps, and present readers with the growth process of a machine learning expert.

I. Academic experience

Gong Yu 199 1 received a bachelor's degree in computer science and technology from Peking University and a doctor's degree in computer science from the University of California at Berkeley on 1996. Worked in Microsoft Research Asia and Stanford University, and joined the Institute of Automation of China Academy of Sciences on 20 15. Research interests mainly include machine learning, deep learning, natural language processing and other fields.

Second, the research direction

Gong's research direction involves many fields, among which machine learning and deep learning are the most prominent. His research achievements in these two fields are widely used in natural language processing, computer vision, intelligent interaction and other fields. His research achievements enjoy a high reputation in the world, and he has won the Best Paper Award in the top international academic conferences for many times.

Third, the operation steps

1. Understand the basic concepts and principles of machine learning, including supervised learning, unsupervised learning and semi-supervised learning.

2. Learn programming languages and machine learning frameworks, such as Python and TensorFlow.

3. Master the skills of data processing and feature engineering, including data cleaning, data preprocessing and feature selection.

4. Choose appropriate machine learning algorithms and models, such as decision tree, support vector machine and neural network.

5. Conduct model training and optimization, including hyperparameter adjustment and cross-validation.

6. Evaluate and test the model, including accuracy, recall and F 1.

7. Apply machine learning model to solve practical problems, such as text classification, image recognition, recommendation system, etc.