Do I need to take training to change careers in deep learning?

With the advent of the era of artificial intelligence, the application of deep learning in various fields has flourished, and many Internet technology companies have also begun to recruit senior talents in deep learning. The high salary and broad prospects of deep learning have attracted more and more people to join. Do college students or working people who want to change careers need to participate in training?

Deep learning is a high-end technology of artificial intelligence, although there are many relevant materials on the Internet, most of them are very basic, and the technology introduced in previous years is also low. If you want to engage in related work or research after studying, it is not enough to rely on self-study, it is best to learn from experienced teachers and do more practical projects to better master deep learning skills.

Training is also a great way for those who want to switch careers to deep learning. Generally speaking, the most important thing in choosing a training institution is the quality of the lecturer, followed by the practical application of the project practice.

Learn to edit! The reason is very simple, easy to learn (unlike other industries with high learning costs and difficulty), suitable for short-term 3-4 months of short-term study, and the industry gap is very large, whether it is looking for a job or taking private orders at home, the monthly income is easily over 10,000, and 20,000 or 30,000 is also commonplace. [Click to enter] free "short** clip post" learning**:

Because of the rise of short ** now, any enterprise, any studio or individual needs to make a large number of short ** to package the brand, send Douyin, send Moments, send ** and other self-** channels to display. Because new content is updated and released every day, there is simply not enough editors to recruit, and the imbalance between supply and demand has caused editors to pay high salaries.

And the editing technology does not require superb computer technology, nor does it require artistic attainments, it is basically a fixed routine, what style of film you want, what rhythm you want, you can easily master it after three or four months of training. If you have a little computer foundation, you will use the mouse to drag and drop, you will click on the icon, and you will save unless you don't want to learn, and you can't learn it. But if you want to learn well, you must find a professional and responsible training institution, and recommend the boss in this field:

Wang's Education. In the processing of "short**editing, short**operation, and special effects", [Wang's Education] is the boss in China, and each city is a headquarters directly operated campus. Different from many other large institutions of the same type: each campus of Wang's Education is physically face-to-face, the teacher is hand-in-hand, and there is a special class teacher from morning to night, and the liver-bursting learning mode will improve quickly, especially suitable for students with zero foundation.

If you are learning from scratch, it is necessary to go to a training school, because professional teachers will give you systematic guidance.

If you are not from a major, such as a master's degree or above in computer science, mathematics, statistics, it is not recommended to transfer. People who change careers in this field are not competitive enough compared to those who come from a professional class.

Hello! If you want to learn and learn technology, you need to be cautious when choosing a school, it is recommended that you first learn about the relevant training institutions or schools online, and then conduct field trips and comparisons, from the teachers, equipment, environment, students, parents and other hardware and software aspects to understand, and at the same time investigate more and compare several similar training institutions and schools! Thank you!

With the development and application of artificial intelligence, big data, Internet of Things, cloud computing and other technologies, deep learning has received a lot of attention from academia and industry. Many universities and research institutes such as the Chinese Academy of Sciences, Tsinghua University, and Peking University have established professional research centers and laboratories to promote the application and development of deep learning in various industries.

The following are some study suggestions for graduate students who are interested in becoming professionals in the field of deep learning, hoping to help students.

First of all, you must learn machine learning first, and then learn deep learning. At its core, machine learning uses algorithms to parse data to learn from it and then make decisions about new data. Deep learning requires proficiency in at least one direction, such as CV or NLP.

Secondly, it is necessary to learn the data structure and mathematical foundation. Mathematics includes probability theory, linear algebra, high mathematics and information theory, and data structures include trees, stacks, linked lists, queues, graphs, etc., while there is an awareness of optimizing the complexity of algorithms.

The third is to continuously strengthen their programming ability, master at least one deep learning tool of tensorflow or pytorch, and further strengthen their hands-on ability by participating in competitions and projects.

In order to help students master deep learning technology faster and become high-end talents in the field of artificial intelligence, Zhonggong Education and experts from the Institute of Automation of the Chinese Academy of Sciences have launched the artificial intelligence "Deep Learning" course, so that the scholars can learn and truly grasp the principles behind machine learning models and algorithms.

It is better to have, there are many deep learning projects at present, but many of them are not very mature, and those who can learn better have to follow those who have enough experience to learn hard;

For example, the deep learning course currently cooperated with the Chinese Academy of Sciences, the Chinese Academy of Sciences is still very in-depth in its research on deep learning, and the project is also mature, and the learning can be learned quickly and solidly.

"Changing careers" to artificial intelligence, you changed careers, right? If you don't participate in the training, you will feel that the efficiency of learning will increase a lot.

If you are just interested in learning, then it is okay not to participate in training, but if you want to work as a job training, there are benefits.

The starting salary will be higher, and the benchmark for salary increases will be much higher after that.

The efficiency will be much higher, self-study may take 1 year, and training may be 4 months, so, to sum up, I still recommend training.

Zhonggong seems to have IT training, the subject can understand, I have learned other courses in their home before is still good, teachers, conditions, and sense of responsibility are full marks, recommended!

As a model of machine learning, deep learning has shown great promise in various fields. The biggest feature of deep learning is that it obtains useful information from a large amount of irrelevant data, and it has been applied in many practical applications: from Netflix's famous movie remorse recommendation system to Google's non-posture promotion, from creative art and literary writing to all kinds of **, deep learning models are being used.

When learning deep learning algorithms, many career changers often feel struggling with the mathematical derivation of the algorithm, unable to understand the physical meaning of the specific core parameter representation, and after understanding the principle of the algorithm, they still don't know how to use the algorithm to solve the actual problem.

The reason for this common phenomenon is mainly due to the lack of solid mathematical foundation and practical experience of learners.

Artificial intelligence is a field in which mathematics, algorithm theory and engineering practice are closely integrated, and it is the embodiment of mathematics, probability theory, statistics, and various mathematical theories. As the mainstream method of realizing artificial intelligence, the most important foundation of machine learning is mathematics and programming. For ordinary programmers, C C++, Python and other programming skills are relatively strong, but the mathematical foundation is relatively weak.

As a tool for expressing and describing machine learning models, mathematics is the cornerstone of in-depth understanding of the principles of machine learning algorithms, and it is also the basic skill of algorithm innovation.