1. Artificial intelligence is a branch of computer science, which tries to understand the essence of intelligence and produce a new intelligent machine that can respond in a way similar to human intelligence. The research in this field includes robot, language recognition, image recognition, natural language processing and expert system. Since the birth of artificial intelligence, the theory and technology are becoming more and more mature, and the application fields are expanding. It is conceivable that the technological products brought by artificial intelligence in the future will be the "containers" of human wisdom. Artificial intelligence can simulate the information process of human consciousness and thinking. Artificial intelligence is not human intelligence, but it can think like human beings, or it may exceed human intelligence.
2. Artificial intelligence is a challenging science, and people engaged in this work must understand computer knowledge, psychology and philosophy. Artificial intelligence is a very extensive science, which is composed of different fields, such as machine learning and computer vision. Generally speaking, one of the main goals of artificial intelligence research is to enable machines to be competent for some complex tasks that usually require human intelligence.
So, what is the content of artificial intelligence?
At present, the learning contents of artificial intelligence major mainly include: machine learning, introduction to artificial intelligence (search methods, etc. ), image recognition, biological evolution, natural language processing, semantic web, game theory, etc.
The compulsory basic courses mainly include signal processing, linear algebra, calculus and programming (with data structure foundation).
From a professional point of view, machine learning, image recognition, and natural language processing are all great directions. As long as you master one of them, it is already very powerful. So don't read a lot of content, some content you just need to master. What you need to choose is a direction of in-depth research. In fact, strictly speaking, artificial intelligence is not difficult to learn, but it is not easy to learn. It needs a certain mathematical foundation and a period of accumulation.
As we all know, now is a gradually intelligent society. With the continuous progress of science and technology, more and more intelligent products begin to enter people's lives. In recent years, I believe that everyone often hears the word artificial intelligence. The industry of artificial intelligence is more attractive and the salary is better. So many college graduates want to enter this industry after graduation, but it is not easy to enter this industry. If it's a zero foundation, you need to learn more. So what do we need to learn from the introduction of artificial intelligence?
What we need to know is that artificial intelligence is a comprehensive subject, involving many aspects, such as neural network, machine recognition, machine vision, robots and so on. Therefore, it is not easy for us to learn the whole artificial intelligence well.
First of all, you need a certain mathematical foundation, such as high numbers, linear algebra, probability theory, statistics and so on. Many people may ask, why should I have a mathematical foundation in learning artificial intelligence? The two seem to be unrelated, but they are not. Linear algebra can let us know how to visualize the research object, probability theory can let us know how to describe statistical laws, and there are many other mathematics disciplines that can make us get twice the result with half the effort when learning artificial intelligence.
Then what we need is the accumulation of algorithms, such as artificial neural network and genetic algorithm. Artificial intelligence itself is an intelligent tool to calculate and simulate things in life through algorithms and finally make corresponding operations. Algorithm plays a very important role in it, which can be said to be an indispensable part.
The last thing to master and learn is the programming language. After all, the implementation of the algorithm still needs programming. Learning Java and Python is recommended. If you want to develop in the direction of big data in the future, you must learn Java, and Python can be said to be a programming language that you must master to learn artificial intelligence. Of course, mastering only one programming language is not enough, because most robot simulations adopt mixed programming mode, that is, using a variety of programming software and language combinations. Assembly and C++ are commonly used in artificial intelligence, in addition to MATLAB and VC++. In a word, programming is an essential skill, which needs us to spend a lot of time and energy to master.
Now artificial intelligence is developing faster and faster, thanks to the rapid development of computer science. It can be expected that in the future, artificial intelligence products will be ubiquitous in our lives, which can bring great convenience to our lives, and the future development prospects of the artificial intelligence industry are also very bright. So there is nothing wrong with choosing the artificial intelligence industry, but as the article said at the beginning, if you want to enter this industry, you need to work hard and master the skills needed by this industry.
1. Mathematical Basis:
Advanced mathematics, linear algebra, probability theory, mathematical statistics and stochastic processes, discrete mathematics, numerical analysis, game theory;
2. Algorithm accumulation:
Neural network, support vector machine, Bayesian, decision tree, logistic regression, linear model, clustering algorithm, genetic algorithm, estimation method, feature engineering, etc.
3. Programming language:
Mastering at least one programming language, the more proficient the better, after all, the realization of the algorithm still needs programming;
4. Technical basis:
Computer principle, operating system, programming language, distributed system and algorithm basis;
Artificial intelligence, namely AI(ArtificialIntelligence), is a comprehensive subject including computer, cybernetics, information theory, neurophysiology, psychology and linguistics.
This concept was first put forward at Dartmouth's academic conference: artificial intelligence is to study how to make artificial intelligence machines or intelligent systems from the perspective of computer application systems, to simulate the ability of human intelligence activities, and to extend human intelligence science.
core curriculum
artificial intelligence
machine learning
Advanced operating system
Advanced algorithm design
computational complexity
mathematical analysis
Advanced computer graphics
Advanced computer network
Employment direction reference
(1) Search direction: Baidu, Google, Microsoft, Yahoo, etc. (including intelligent search, voice search, picture search, video search, etc. Are the future directions).
(2) Medical image processing: Many medical equipment and instruments will involve image processing and imaging. Big companies include Siemens, General Electric and Philips.
(3) Direction of computer vision and pattern recognition: fingerprint recognition, face recognition, iris recognition, etc. Mentioned above; Another general direction is license plate recognition; At present, video surveillance is a hot issue, and it is also good to track and identify it.
(4) There are also some companies that need talents in image processing, such as VIA, Panasonic, Sony and Samsung.
In addition, the talents in the AI direction are all high-tech, and the treatment is naturally relatively rich, so this direction is very promising.
Advanced mathematics, linear algebra, probability theory, mathematical statistics and stochastic processes, discrete mathematics, numerical analysis. The basic knowledge of mathematics contains the basic ideas and methods to deal with intelligent problems, and it is also an essential element to understand complex algorithms. At present, all kinds of artificial intelligence technologies are based on mathematical models. To understand artificial intelligence, we must first master the necessary basic knowledge of mathematics. Linear algebra formalizes the research object, and probability theory describes the statistical law.
Need the accumulation of algorithms:
Artificial neural network, support vector machine, genetic algorithm, etc. Of course, algorithms are needed in various fields. For example, to learn SLAM, in order to let robots navigate in the positioning environment and build maps. In short, many algorithms need time to accumulate.
Need to master at least one programming language:
For example, C language, MATLAB and so on. After all, the implementation of the algorithm still needs programming; If you go deep into hardware, some basic courses of electricity are essential.
Learning artificial intelligence needs mathematical foundations: advanced mathematics, linear algebra, probability theory, mathematical statistics and stochastic processes, discrete mathematics and numerical analysis.
Need the accumulation of algorithms: artificial neural network, support vector machine, genetic algorithm, etc. Of course, algorithms are needed in various fields. For example, in order to make robots navigate and build maps in the positioning environment, it is necessary to study SLAM. In short, many algorithms need time to accumulate.
You need to master at least one programming language: after all, the realization of the algorithm still needs programming; If you go deep into hardware, some basic courses of electricity are essential.
First of all, Python basics.
Second, mathematical foundation, including calculus foundation, linear algebra and probability statistics.
Third, various frameworks, such as Tensorflow.
Fourthly, deep learning includes machine learning foundation, deep learning foundation, convolutional neural network, circular neural network, generative antagonistic neural network and deep reinforcement learning.
5. Commercial projects in actual combat, such as mt CNN+ center lost face detection and face recognition, YOLO V2 multi-target multi-category detection, GLGAN image missing part completion, language awakening, etc.
Proficient in C programming language and one of C++, Java and Visual Basic.
From a professional point of view, machine learning, image recognition, and natural language processing are all great directions. As long as you master one of them, it is already very powerful. So don't read a lot of content, some content you just need to master. What you need to choose is a direction of in-depth research. In fact, strictly speaking, artificial intelligence is not difficult to learn, but it is not easy to learn. It needs a certain mathematical foundation and a period of accumulation.
Thank you for your questions. I feel very honored to be able to answer them.
1. Artificial intelligence is a branch of computer science, which tries to understand the essence of intelligence and produce a new intelligent machine, which can respond in a way similar to human intelligence. The research in this field includes robot, language recognition, image recognition, natural language processing and expert system. Since the birth of artificial intelligence, its theory and technology have become increasingly mature, and its application fields have been expanding. It is conceivable that the technological products brought by artificial intelligence in the future will become the "container" of human intelligence. Artificial intelligence can simulate the information process of human consciousness and thinking. Artificial intelligence is not human intelligence, but it can think like human beings and may surpass human intelligence.
2. Artificial intelligence is a challenging science, and people engaged in this work must understand computer knowledge, psychology and philosophy. Artificial intelligence is a very extensive science, which is composed of different fields, such as machine learning and computer vision. Generally speaking, one of the main goals of artificial intelligence research is to enable machines to be competent for some complex tasks that usually require human intelligence.
So what has artificial intelligence learned?
At present, the learning contents of artificial intelligence major mainly include: machine learning, introduction to artificial intelligence (search methods, etc. ).), image recognition, biological evolution, natural language processing, semantic web, game theory, etc.
The compulsory basic courses are mainly signal processing, linear algebra, calculus and programming (with data structure foundation).
From a professional point of view, machine learning, image recognition and natural language processing are all general directions. As long as you are proficient in one of them, you are already very powerful. So don't watch too much. You just need to master some things. You need to choose a direction to study deeply. In fact, strictly speaking, artificial intelligence is not difficult to learn, but it is not easy to learn. It needs a certain mathematical foundation and a period of accumulation.