What fields can be applied to deep learning for better employment?

For example, in the field of transportation, deep learning technology can detect vehicle parking, wrong-way driving and other behaviors, and even accurately identify the license plate number, color, model, and people in the vehicle to assist traffic law enforcement, and even call the police in the event of traffic accidents and traffic congestion.

For example, in the financial industry, banks can use deep learning technology to analyze millions of consumer data (age, occupation, marital status, etc.), financial borrowing and insurance status (whether there is a default record, repayment time, vehicle accident history, etc.) to determine whether they can provide loan services.

For example, in the home furnishing industry, deep learning technology is also used in the application of smart homes, such as smart refrigerators record the types of ingredients and users' daily diet data through image recognition and other technologies, and then analyze the user's eating habits and give the most comprehensive healthy diet suggestions based on multiple dimensions.

For example, in the manufacturing industry, machine vision has been used in industrial automation systems for a long time, such as intelligent integration testing of instrument panels, automatic damage control of metal plate surfaces, automobile body inspection, banknote printing quality inspection, metallographic analysis, assembly line production inspection, etc., machine vision automation equipment can replace manual tireless repetitive work, and in some dangerous working environments that are not suitable for manual work or artificial vision is difficult to meet the requirements, machine vision can replace artificial vision.

Log in in the upper right corner of the page + join the study**.

If you want to find a deep learning experience, you can apply this to some learning platforms, and then improve your learning cognition.

If you study in Shenzhen, you can learn some electronics, and if you study carefully, you can learn some electronics, which is still very popular now.

Regardless of the fields in which deep learning can be applied, it depends on what fields it is applicable to, and I think it is entirely based on these situations, and then I understand.

What is deep learning, what are the job prospects, and how much do you use it in your work?

Hello, deep learning is just an attitude towards learning hard work, that is, to learn a certain content to a deeper level, for the relevant content can be used flexibly, not a certain discipline, so there is no employment prospects, deep learning will be used a lot in any industry, especially when you have just come into contact with a certain industry, deep learning should accompany the work at any time, I hope I can help you,

Deep Learning Engineer Career Path.

At present, there are two paths to becoming a deep learning engineer: one is through campus recruitment in spring and autumn, and the other is cross-industry transformation with the help of social recruitment. The aforementioned article once divided deep learning engineers into algorithm engineers, back-end engineers, and front-end engineers.

According to the results of the recruitment and consulting reports of major companies in the market, engineers who have been working for about 3-5 years are the main force in the artificial intelligence market, and more fresh graduates are still growing.

Employment of deep learning engineers.

To start this topic, let's first take the example of a deep learning engineer around us to analyze big data.

Student A, whose undergraduate major belongs to engineering, has a strong interest in deep learning, then chose to study on his own and signed up for training classes at the same time, and after graduating from graduate school, he joined a start-up company, but the follow-up work has nothing to do with deep learning.

Xiao B joined the algorithm engineer in the direction of NLP natural language processing last year, and studied in 985 universities in China from bachelor's degree to doctorate, and then entered a large factory and finally became a deep learning engineer, focusing on algorithms.

Here you can find that in fact, there is still a big difference between individual cases. Therefore, the small pp reviewed the reports of recruitment agencies and authoritative consulting agencies, and analyzed and summarized the following content for everyone.

Deep learning is a branch of machine learning, and big data in the information age provides a broad application for deep learning. It is foreseeable that deep learning will continue to gain momentum in the coming decades.

For students in school or those who want to change careers, if they want to develop in deep learning, what are the future career development directions? The main ones are as follows.

1) Deep Learning Engineer. He is mainly responsible for the research and development of algorithms and systems such as deep learning framework construction, machine learning, image processing, etc., and supports the research of the company's related products in the field of deep learning.

2) Machine vision R&D engineer. Mainly engaged in technology research and development and engineering implementation in the field of image analysis and understanding, applying deep learning technology to specific fields such as face recognition, OCR, object detection, classification, segmentation, etc., building and optimizing deep learning models, and improving the effect, performance and ease of use.

3) Speech Recognition Engineer. He is mainly responsible for the algorithm optimization of the core model of speech recognition, tracking the industry's leading speech recognition algorithm technology, and promoting the progress of speech recognition research.

4) Autonomous driving engineer. He is mainly responsible for the design and implementation of highly reliable autonomous driving software systems, system optimization and maintenance, standardizing and refining software development according to the functional requirements of autonomous driving, completing the construction of the computing platform software development environment, transplanting algorithms to designated hardware platforms, and optimizing performance.

The application of AI technology, the industries in which AI technology has been strongly implemented, the engineers required for AI technology, and the AI technology direction that is worth investing in in the future.

Deep learning refers to the depth and breadth of knowledge that we need to deepen in the learning process. Only with solid professional knowledge will our employment prospects be better.

What is depth of knowledge?

Is it? What are the job prospects, and how much do you use in the job? What is this old Shensheng's student? What are the job prospects?

What is the right? Do you use it much at work? So which of you study what?

There are many kinds of job prospects, so there are various kinds of in-depth learning, and there are also various job prospects, so there are all kinds of different ones in the public. Deep learning for job prospect communication.

What is deep learning, I think deep learning is to understand Shenzhen consulting through a thing that you are interested in.

Deep learning is mainly for college students majoring in computer science and working people in the IT industry, as well as working people who need to sprint for high salaries.

In the form of online live classes, three days a week, a total of 30 class hours, almost a month.

It is a way to live online learning.

It seems that classes start in mid-June, and now there is a discount on tuition when you sign up.

The course is 30 hours, three days a week, and lasts for 5 weeks.

At present, it is in the form of online live classes, but screen recording is also provided, which can be repeated**.

The mode of live online class, three days a week, will be live, and some python-based online courses will be sent, and the quality of this course is still very good.

The deep learning live course of excellent employment is jointly developed by experts from the Institute of Automation of the Chinese Academy of Sciences, and the course contains 6 practical projects, all of which are from the project practice of enterprises. What are the actual projects? Let's introduce it to you

Project 1: Handwritten number recognition project practice.

This project is based on Tensorflow, the most popular open-source deep learning framework

In order to realize handwriting digit recognition, multi-layer convolutional neural network is used to extract the features of handwritten digits, and fully connected neural networks are used to recognize handwritten digits. The whole project process includes data analysis and processing, model structure design, optimization and debugging, and result analysis, etc., and the final recognition accuracy is achieved.

More than 90%. This technology can be applied to text data recognition scenarios, such as card text data recognition, bill text data recognition, and automobile scene text recognition.

Project 2: Practical Combat of Textual Feature Vectorization of Literary Works.

This project focuses on the application of deep learning in natural language processing, using recurrent neural networks and long short-term memory networks to achieve word embedding learning and contextual inference in this field. In this project, we will select some texts of literary works to realize word embedding feature extraction and contextual inference based on long short-term memory. The relevant technology can be used for the processing of series data with temporal and spatial attributes, such as economic data, data, and consumer consumption behavior data.

After learning, it can be directly applied to tasks such as intelligent customer service dialogue generation, visual image synthesis, and data augmentation. This project will take face generation as an example to introduce the principle and implementation of generative adversarial networks.

The data throughput of deep learning is improved in parallel and the learning and training process of the model is accelerated. This project introduces GPUs for deep learning based on face generation

and distributed clusters in parallel mode. The related technology can be directly applied to various scenarios of artificial intelligence + big data cloud computing.

Project 5: Practical practice of maze game project based on deep reinforcement learning.

This project will briefly introduce the basic ideas of reinforcement learning and demonstrate the development and training process of deep reinforcement learning through the practice of game mazes to realize AI

The system learns and makes intelligent decisions on the environment. Related technologies can be used to assist decision-making tasks such as autonomous driving, AI quantitative investment, e-commerce product recommendation, robotics, human-computer interaction, and optimal scheduling.

Project 6: Enterprise-level license plate recognition project real station.

This project will take license plate recognition as a practical application, and guide students to complete the whole process implementation of typical artificial intelligence projects, including project positioning in demand analysis, system architecture design, functional module implementation, key algorithm application, testing and maintenance, etc. The project will focus on the core.

The development and testing of AI modules can familiarize students with the full cycle of actual enterprise-level projects. The technical core of this project can be extended to the identification of other similar problems, such as container number recognition, and can also be used as one of the core modules of the smart parking project.

There are a lot of industry knowledge points involved in the whole course.

Let's take a look at what's there:

Overview of AI and introduction of cutting-edge application achievements.

Principles of artificial neural networks and convolutional neural networks and tensorflow real-life recurrent neural networks and project practice.

Principles of Generative Adversarial Networks and Project Practice.

Distributed processing of deep learning and project practice.

Deep reinforcement learning and project practice.

Enterprise-level project practice - license plate recognition project practice.

An introduction to the latest cutting-edge technologies in deep learning.

The overall setting of the course is gradual, layer by layer, and the setting is still more in line with the general learning route.

Mainly learn the knowledge of artificial intelligence, such as: artificial neural network BAI and convolutional neural network principles and TensorFlow practice; Principles of Recurrent Neural Networks and Project Practice; Principles of Generative Adversarial Networks and Project Practice. Distributed processing of deep learning and project practice. Deep reinforcement learning and project practice. Enterprise-level project practice - license plate recognition project practice, etc.; These are suitable for some students with basic learning, and are not suitable for beginners.

Hello! Learn to be a man first, honor your parents, and then learn to do things down-to-earth, there is no Amitabha Buddha in the south!

In-depth study of excellent employment can learn interpersonal communication, as well as basic knowledge and theoretical knowledge of various majors.

Students who need to have a certain foundation and related majors can learn.

What do you learn in deep learning? I think it's theoretical, practical, and practical.

In order to allow students to better grasp deep learning technology and become senior talents in the field of artificial intelligence, Zhonggong Education has developed and launched a deep learning course with experts from the Institute of Automation of the Chinese Academy of Sciences. With high-quality teachers + cutting-edge technology + service guarantee, the course has received the attention and welcome of many students as soon as it is launched.

Some friends may be curious, what are the advantages of the deep learning course of Zhonggong Education? Why is it attracting so much attention and learning? There are three main aspects.

1) Face the artificial intelligence industry standard setter, and the expert team of the Institute of Automation, Chinese Academy of Sciences will broadcast the whole process live and personally guide the teaching and practice. The lecturer has presided over the National Natural Science Center, participated in a number of national scientific research projects, and published artificial intelligence monographs.

2) Practical operation of real enterprise-level projects, facing the complex development environment, getting rid of the idealized development of open source projects, and more in line with the real needs of enterprises.

3) The technology keeps up with the market demand, and the landing field is wide, not limited to cutting-edge technologies such as speech recognition, image recognition, machine dialogue, etc., covering 75% of the technical points in the industry, and meeting all kinds of employment needs.

During the 5-week course, you will have a comprehensive understanding of AI deep learning, the principles of artificial neural networks, convolutional neural networks, recurrent neural networks, generative adversarial networks, and distributed processing of deep learning, which can be applied to enterprise-level projects. At the same time, the source code of the enterprise-level projects in the course will be given away to help you seamlessly connect with the key points of the course and master the necessary skills of talents in large factories in minutes.