How does AI learn?

Artificial Intelligence (Artificial Intelligence), the English abbreviation is AI. It is a new technical science that studies and develops theories, methods, technologies and application systems for simulating, extending and expanding human intelligence.

With the growth of our national strength and the advancement of science and technology. One of the most popular trends at present is the rise of AI and big data analysis. So how to learn AI happily, quickly and methodically? There is also a shortage of this type of talent in the market, so where should you start? Here I will introduce to you how to learn AI?

1: Basics of Mathematics. To learn AI, you must master the most basic advanced mathematics, line generation, and probability theory. You must at least know Gaussian functions, matrix derivation, and understand what gradient descent is. Otherwise, you will not be able to understand the basic principles of the model at all, and it will be difficult to participate in model adjustment and training. There is no way to talk about it.

2: Basics of Programming. Of course, if you are a pure algorithm researcher, the requirements for engineering ability will not be too high, but you also need to be able to write source code; and for engineers doing algorithm engine development or application development, the ability to implement code will directly determine Quality and efficiency of work output. Therefore, if you want to be an AI engineer, you need to be proficient in at least one programming language, as well as supporting tools, common libraries, etc. (Related recommendations: "Python Tutorial")

3: Machine Learning Basics. Since this round of artificial intelligence boom comes from the outstanding performance of deep learning-related technologies and applications, the most popular position for recruitment is undoubtedly machine learning algorithm engineer. Therefore, classic machine/deep learning algorithms, common neural network models, model parameter adjustment and training techniques need to be mastered as much and in depth as possible.

Four: Basic knowledge in professional fields. The main application areas of artificial intelligence can be roughly divided into images, speech and NLP (natural language processing). No matter which field you are in, there is a huge amount of professional knowledge that needs to be mastered. For example, if you want to work in machine vision in the intelligent driving industry, then you need to master image-related knowledge; and if you want to make a smart speaker For algorithm development, you need to master speech and NLP related knowledge.

Five: In-depth knowledge of specific industries. Any application scenario has its own unique data structure, and a practical AI application is naturally inseparable from an in-depth understanding of the business itself. Algorithm engineers need to clearly understand which modules an AI system consists of, what are their interrelationships, what technologies are used, and what problems are solved. Only then can they conduct experimental research on specific problems and optimize them.

This article is an introduction to how to learn AI. I hope it will be helpful to friends in need!

The above is the detailed content of How does AI learn?. For more information, please follow other related articles on the PHP Chinese website!