Originally, I didn't intend to write such a thing, although I saw it several times. What should I learn in artificial intelligence majors? Which school should I go to to study artificial intelligence? I didn't answer such similar questions, because I'm afraid I'm just trying to persuade them to quit. But now a friend around me asks me that the child of a relative in my family is interested in the major of artificial intelligence, and asks me how I am, sighing, while most of the application for the exam is not over yet, I will talk about my views, hoping to be useful to everyone , to provide you with some ideas.
1. What is the course setting of artificial intelligence?
CSDN has talked a lot about this issue, so I won't show my ugliness, but in general I agree: If you really want to apply for the exam, then go for the postgraduate entrance examination, don't go out to find a job after finishing your undergraduate degree. Moreover, the current cv and nlp algorithms that have achieved good results, deep learning is the mainstream. If you want to do artificial intelligence, first see how many GPUs the school you want to apply for can afford. Of course, these may not be involved in the undergraduate stage, but I just want everyone to understand that first of all, not so many colleges and universities have the experience and teachers to do these things. Secondly, the things learned at the undergraduate stage are too basic, and this knowledge is not enough to support employment. To get in touch with in-depth content, either go to graduate school or apply for off-campus training.
2. Another way of thinking: learn basic subjects such as mathematics and physics first, lay a good theoretical foundation, and then transfer to artificial intelligence.
I think there are two problems with this kind of thinking: a. A friend of the master’s undergraduate C9 physics experiment class, the graduate student transferred to Top2 to do algorithm algorithms, according to my communication with him: juniors and seniors have made up a lot of computer core courses, while making up courses Complementing the code is quite painful. At the same time, although the teachers who do algorithms like to ask for students from the Department of Mathematics, the mainstream of postgraduate entrance examinations are still students majoring in computer science. Many of his classmates want to switch to computer or finance, but the success rate in the end is not enough. 10%. b. The reason why this type of basic discipline has endured for a long time is really because the essence of many disciplines is mathematics, and logical thinking is very important, especially at the doctoral level. But to put it bluntly, "Your uncle will always be your uncle", not everyone can learn subjects like mathematics and physics, especially when it comes to scientific research, the little mathematics you learn in class is not enough. You are good at mathematics and physics in high school, your high score test and your suitability for this kind of basic subjects are completely two concepts, especially how this kind of subjects can only reflect the accumulated value after they have passed the postgraduate stage. So in a word, you think you are really good and like it, and you are bold in it. Basic subjects need really good talents, but I guess that most ordinary people like me can still watch from a distance and not play around. Pick the flowers of Gaoling carefully. Need to consider: If there is no postgraduate study and you are not given the opportunity to shine in another field, what kind of job are you looking for?
3. Artificial intelligence is good for employment.
As for the current employment situation, algorithms are indeed better than many other majors, and at least the salary level is still online.
But what I am afraid of is that the AI industry will become more and more complicated in the future, and it is only a graduate student. If artificial intelligence can be mass-produced at the undergraduate level in the future, it is hard to imagine how fierce the competition in the job market will be. I personally think that artificial intelligence is not only for undergraduates, but also for graduate students who have transferred from other majors. I am not particularly optimistic about the "AI for All". In fact, I have also met some people in the industry. The general reaction is that the company has pitfalls and some people submit resumes, but it is really difficult to match. In this way, it cannot be said that the supply exceeds demand, but the supply and demand do not match. It is hard to say whether the newly opened artificial intelligence major can match supply and demand.
Therefore, if you really want to enter the industry, get there early, and don't wait until the real industry is saturated before going to work with them.
Finally, let’s add some ink marks. In fact, I am not optimistic about the direction of artificial intelligence. This direction must be the core competitiveness of future development, and it is also a force that is strongly supported by the state. Algorithms have improved certain industries, but I am afraid that children will be blindly optimistic about the market and enter the industry easily. If you are interested in artificial intelligence, it is recommended to choose a computer-related major for undergraduates. Undergraduate education is fundamental education, and graduate students have time to do artificial intelligence. Most people who do algorithms are such majors. Artificial intelligence is the best, otherwise you can be a programmer.
Finally, I will share with you some artificial intelligence learning materials I have compiled for free. It has been compiled for a long time and is very comprehensive. Including some artificial intelligence basic introductory videos + AI common framework practical videos, image recognition, OpenCV, NLP, YOLO, machine learning, pytorch, computer vision, deep learning and neural network and other videos, courseware source code, well-known domestic and foreign elite resources, AI popular Papers, etc.
The following are some screenshots, scan the code at the end of the article and add me to get it for free (I can also answer one or two questions about AI technology)
Table of contents
1. AI Free Video Courses and Projects
2. Artificial intelligence must-read books
3. Collection of Papers on Artificial Intelligence
4. Machine Learning + Computer Vision Basic Algorithm Tutorial
Five, deep learning machine learning cheat sheet (a total of 26)
To learn artificial intelligence well, you need to read more books, do more hands-on work, and practice more. If you want to improve your level, you must learn to calm down and learn systematically slowly, so that you can gain something in the end.