What is the main thing to learn in artificial intelligence?

Artificial intelligence is very difficult to learn, and the main direction of artificial intelligence is deep learning, which involves a lot of mathematical content, and it is no longer a simple and pure programming problem. If you need to learn artificial intelligence technology, it is recommended to choose [Danai Education]. Ways to learn artificial intelligence:

1. Lay a good foundation and learn advanced mathematics and [python programming language]. Advanced mathematics is the basis for learning artificial intelligence, because artificial intelligence will design a lot of data and algorithms, and these algorithms are mathematically derived, so to understand algorithms, you need to learn some knowledge of higher mathematics first. First, students will learn the basics of advanced mathematics, starting with basic data analysis, linear algebra and matrices.

2. Advance to the stage and start learning machine learning algorithms. Once you have mastered the above basics, you need to start learning machine learning algorithms...

, Fundamentals of Mathematics. The basic knowledge of mathematics contains the basic ideas and methods for dealing with intelligent problems, and it is also a necessary element for understanding complex algorithms. This module covers the fundamentals of mathematics necessary for artificial intelligence, including linear algebra, probability theory, optimization methods, etc.

2. Machine learning. The role of machine learning is to learn learning algorithms from data to solve practical application problems, which is one of the core contents of artificial intelligence. This module covers the main methods in machine learning, including linear regression, decision trees, support vector machines, clustering, and more.

3. Artificial neural network. As a branch of machine learning, neural networks introduce cognitive science into machine learning to simulate the interaction of biological nervous systems in the real world with good results. This module covers the basic concepts of neural networks, including multilayer neural networks, feedforward and backpropagation, self-organizing neural networks, and more.

4. Deep learning. Put simply, deep learning is a neural network with multiple middle layers, and the rise of deep learning has been fueled by soaring data** and computing power. This module covers the concepts and implementations of deep learning, including deep feedforward networks, regularization in deep learning, autoencoders, etc.

5. Examples of neural networks. Under the framework of deep learning, some neural networks have been used in various application scenarios and have achieved good results. This module covers several examples of neural networks, including deep belief networks, convolutional neural networks, recurrent neural networks, etc.

6. Artificial intelligence beyond deep learning. Deep learning has both advantages and limitations, and artificial intelligence research in other directions is a useful complement. This module covers typical learning methods that are not related to deep learning, including probabilistic graph models, cluster intelligence, transfer learning, knowledge graphs, etc.

7. Application scenarios. In addition to replacing humans with repetitive labor, artificial intelligence also provides meaningful attempts in the processing of many practical problems. This module covers the application of AI technology in several types of practical tasks, including computer vision, speech processing, dialogue systems, etc.

1. Linear Algebra: How to Formalize the Research Object?

2. Probability theory: how to describe statistical laws?

3. Mathematical Statistics: How to See Big from Small?

What do you study in Artificial Intelligence?

At present, the learning content of artificial intelligence majors includes: machine learning, introduction to artificial intelligence (search method, etc.), image recognition, biological evolution theory, natural language processing, semantic web, game theory, etc.

The pre-required courses are mainly used, such as signal processing, linear algebra, calculus, and programming (with a foundation in data structures) Judging from the above professional course content, there are still a lot of artificial intelligence-related knowledge content that needs to be mastered.

From a professional point of view, machine learning, image recognition, and natural language processing, any of these are a major direction, and as long as you are proficient in one of them, it is already very bad. So don't look at a lot of content, some you just need to master, what you need to choose is a direction to delve into. In fact, strictly speaking, artificial intelligence is not difficult to learn, but it is not easy to learn, it needs to have a certain foundation related to mathematics, and there is also a period of accumulation.

There are many employment directions for artificial intelligence majors, such as: mechanical manufacturing, scientific research, engineering development, computer science, software engineering, applied mathematics, electrical automation, communication, etc.

On the one hand, the research and development of artificial intelligence is more difficult, and on the other hand, the research and development of artificial intelligence imitation field requires more research resources, and the talent training cycle is relatively long. Since artificial intelligence is still in the early stage of industry development, if you want to have a better job exit by studying artificial intelligence, you can consider studying for graduate school.

Artificial intelligence has now been included in the national development plan, and the country has put forward a three-step development strategy for artificial intelligence. Therefore, in the future development, it will definitely become more and more hot. According to the global AI talent distribution released by LinkedIn, there is a shortage of AI talents in China before the shouting of more than 50,000.

Talent is in extreme short supply. From scientific research institutes to business giants and enterprises, all walks of life are developing and introducing artificial intelligence, resulting in a very large gap in the field of artificial intelligence. Moreover, as a high-end technology based on computer technology, the salary will never be low, not only will it not be low, but it will be very high.

The courses that AI needs to learn are as follows:

Artificial intelligence majors mainly need to learn: "Artificial Intelligence, Society and Humanities", "Philosophical Foundations and Ethics of Artificial Intelligence", "Advanced Robot Control", "Cognitive Robots", "Robot Planning and Learning", "Bionic Robots", "Swarm Intelligence and Autonomous Systems", "Unmanned Technology and System Implementation", "Game Design and Development", "Computer Graphics Zhengyun Bureau Studies", "Virtual Reality and Augmented Reality", "Modern Methods of Artificial Intelligence I".

Employment prospects

The prospect is very good, China is upgrading its industry, and industrial robots and artificial intelligence will be strong hot spots, and it will be exactly 3 5 years later. The difficulty, it must be high, you need to have innovative thinking ability, calculus, number series, etc. in high mathematics must be very good, software programming (the most widely used language in the basics: C C++) must be very good, microelectronics (digital circuits, low-frequency and high-frequency analog circuits, and most importantly, embedded programming ability quietly branches) must be learned very well.

It is also necessary to have a certain mechanical design ability (spatial thinking ability is very important). In this way, you are a talent, and you are a talent in the field of artificial intelligence that China urgently needs in the next five years. If you delve deeper, you will be an expert or even a master in the field.

Netizen 2: Artificial intelligence is based on computer technology, relying on algorithms and imitating the structure of human brain neurons, under the statistics of big data, using high-level computer language Python and other x86 or Linux architecture systems to write deep learning, relying on graphics massive AI GPU groups and CPU and other architecture of high-precision sensors on the intelligent electronic artificial intelligence similar to human brain thinking.