3 Real JD NLP Projects That Boost Resume Acceptance Rate by 200%

JD NLP Enterprise Project Practical Training Camp

Focusing on Cultivating the Top 10% NLP Engineers in the Industry

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01 Detailed Explanation of JD NLP Project Content

△If you have questions about the course project content, you can watch this video

02 Scientific Practical Arrangement

Each training camp has a rigorous scientific arrangement, with a series of courses every week including theory, practical exercises, case sharing, project explanations, and more.

△ Excerpt from part of the course arrangement

03 Project Explanation & Practical Assistance

The ultimate goal of the training camp is to help students complete projects and understand the core knowledge and skills involved in the projects. A large amount of time will be spent helping students understand the projects and the practical explanations involved.

△ Excerpt from course project

04 Best Engineering Practices

Experts from JD AI and other industry professionals will discuss best engineering practices in the industry, such as AI model deployment, code writing, model tuning, and debugging techniques.

△ Source from JD Zhilian Cloud AI module architecture diagram

05 Professional Paper Interpretation

As an AI engineer, the ability to read papers is essential. In the course, we will arrange a classic English article for students to read every 1-2 weeks, which will then be interpreted by the instructor.

△ Excerpt from part of the paper arrangement

06 Code Interpretation & Practical

For core models like BERT and XLNet, careful arrangements for code interpretation and practical classes will be made to help students deeply understand the details and have the capability to implement them.

△ BERT model code practical explanation

07 Industry Case Sharing

During the training camp, we will invite collaborating experts to share industry cases and technical solutions, such as building knowledge graphs and customer service systems in the insurance field.

Below is a share from Dr. Zeng↓↓↓

“Google YouTube Video Recommendation Based on Deep Learning”

Guest Introduction: Dr. Zeng

Expert in computer vision and machine learning

Has published over 30 papers in CVPR, ACMMM, TPAMI, SCI journals, and EI conferences

△ Expert live sharing

08 Daily Community Q&A

To help solve problems encountered by students, professional teaching assistants will provide all-day community Q&A services. Our teaching assistants come from leading AI companies and prestigious universities at home and abroad, and solid theoretical and industrial application backgrounds are important criteria for selecting teaching assistants, rejecting empty theoretical discussions.

△ Professional answers from teachers in the community

09 Daily Assignments & Explanations

To consolidate some core knowledge points, students will need to complete daily small assignments in addition to the major projects. Afterward, teaching assistants will provide detailed answers.

△ Small assignments in course learning

Who is this course suitable for?

University Students:

Undergraduate/research/PhD students in computer science or related fields who wish to work in AI-related jobs after graduation.
Those who want to hone their skills in real industrial scenarios and enhance their workplace competitiveness.
Those who wish to apply for master’s or PhD programs at prestigious universities at home and abroad after graduation.

Working Professionals:

Individuals with a good engineering research and development background who wish to work on AI-related projects or jobs.
Those engaged in AI work who want to further enhance their practical experience in NLP.
Those working in NLP who wish to gain a deeper understanding of model mechanisms.
AI developers who want to break through technical bottlenecks and understand cutting-edge information in NLP.

Admission Standards:

Undergraduates, master’s students, or PhD students in engineering and science-related majors or working professionals in the IT field
Strong hands-on ability, proficient in Python programming
Familiarity with basic machine learning algorithms (logistic regression, random forest, SVM) or practical experience
Good English reading ability, at least reaching CET-4 level