Author: Zen and the Art of Computer Programming
Knowledge and skills can be defined along different dimensions, but generally speaking, knowledge falls into three broad categories: technology, management, and economics. Skills include interview ability, problem-solving ability, communication and coordination ability, etc. The role of an architect not only covers technology, management and economic knowledge and skills, but also requires a high degree of leadership, business understanding, innovation awareness and product design capabilities. Therefore, architects are senior technical personnel with much more responsibilities and requirements than other technical positions. However, due to the wide and complex business scope of the company, architects often need to learn across borders to master various business-related knowledge. Under such circumstances, architects are also faced with a new learning problem - how to quickly improve their professional capabilities? In this article, the author will start from the four aspects of architect learning, and introduce how to improve the professional ability of architects through interdisciplinary learning: technical ability, business ability, management ability and personal ability.
2. Explanation of basic concepts and terms
To make it easier for the reader to understand what the author is saying, here is a brief description of some key concepts and terms:
- Cross-disciplinary learning: refers to learning knowledge and skills between different fields. The author believes that the connection between different fields is very important. For example, business personnel learn technical knowledge, managers learn economic knowledge, and add their own personal knowledge to form a complete knowledge system.
- Deep Learning: An algorithm in the field of machine learning that uses neural networks to train models to learn features from massive data and predict target variables based on features. It was originally proposed by Hinton and his colleagues in 2006.
- Unsupervised Learning (Unsupervised Learning): A method in machine learning that does not require labeled data and can automatically analyze the structure and laws of data. Unsupervised learning is used to deal with the lack of labeled or labeled data, such as clustering, recommender systems, etc. </