3 JD AI NLP projects that can be included in your resume. After these five steps, you will become a top algorithm engineer

Yunzhong sent this from Aofei Temple
Edited by QuantumBit | Public Account QbitAI

How to get started with NLP? How to apply theoretical knowledge in real cases? How to become a top 10% NLP engineer in the industry? How to plan the career development of AI engineers, and what projects do first-tier AI companies have?

JD NLP Enterprise Project Practical Training Camp Here it comes! A one-stop solution to all the above problems.

Jointly created by JD AI and Greedy Academy, the course is taught by top instructors from industries such as Google, Amazon, Microsoft, and JD .

Select the most representative JD AI enterprise projects and use real project data for practical teaching;

Five stages One-stop teaching from theory to practice, from project practice to interview preparation, covering core skills in the field of NLP (feature engineering, classification models, syntax trees, etc.), cutting-edge technologies (BERT, XLNet, Seq2Seq, Distillation, Transformer, model compression , etc.);

Outstanding students will receive a direct ticket to the JD AI interview .

Students who are interested in learning more about the course and signing up for it are welcome to click to read the original text, or add the course girl on WeChat for specific consultation.

class schedule

Mentor lineup

Teaching system

1. Scientific practical arrangements

Each training camp has a rigorous and scientific arrangement, with a series of courses including theory, practice, case sharing, project explanation, etc. every week.

△ Excerpt from some course schedules

2. Project explanation & practical help

The ultimate goal of the training camp is to help students complete the project and understand the core knowledge and skills contained in the project. A lot of time will be spent in the training camp to help students understand the project and the practical explanations involved.

△ Excerpt from the course project

3. Best engineering practice

Industry experts from JD AI and other companies will talk about the best engineering practices in the industry, such as AI model deployment, code writing, model parameter adjustment, and debugging techniques.

△ From the architecture diagram of a module of JD Zhilian Cloud AI

4. Professional paper interpretation

As an AI engineer, the ability to read papers is a must. In the course, we will arrange a classic English article for students to read every 1-2 weeks, and then the teacher will help interpret it.

△ Excerpt from some paper arrangements

5. Code interpretation & practice

For core models such as BERT, XLNet will carefully arrange code interpretation and practical courses to help students deeply understand its details and be able to implement it.

△ Practical explanation of BERT model code

6. Industry case sharing

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

The following is a sharing from Dr. Zeng↓↓↓

"Google YouTube Video Recommendation Based on Deep Learning"
Guest Profile : Dr. Zeng, an expert in computer vision and machine learning. He has published more than 30 papers in CVPR, ACMMM, TPAMI, SCI journals, EI conferences, etc.

△ Expert live sharing

7. Daily community Q&A

In order to help students solve problems, professional teaching assistants will provide all-day community Q&A services. Our teaching assistants are all from first-tier AI companies and famous domestic and foreign universities. Solid theory and industrial application are also important criteria for us to select teaching assistants. We refuse to talk about theory in vain.

△ Community Professional answers from teachers in the group

8. Daily homework & explanation

In order to consolidate some core knowledge points, students need to complete daily small assignments in addition to large projects. The teaching assistant will then give detailed answers.

△ Small homework during course study

Graduate destination

Suitable for

College Students:

Undergraduate/research/doctoral students in the field of computer or information, who hope to work in AI-related jobs after graduation.
They hope to hone their skills in real industrial scenarios and improve their workplace competitiveness.
After graduation, they hope to apply for a master's or doctoral degree at a prestigious university at home or abroad.

Working people:

Have a good engineering R&D background, and hope to engage in AI-related projects or work.
Engaged in AI work, hope to further improve NLP practical experience.
Engaged in NLP work, hope to have a deep understanding of the model mechanism.
AI developer, hope to break through technical bottlenecks and understand the cutting-edge information of NLP.

Admission criteria:

1. Undergraduates, masters or doctoral students in science and engineering or professionals in IT field
2. Strong hands-on ability and proficiency in Python programming
3. Familiarity with basic machine learning algorithms (logistic regression, random forest, SVM) or practical experience
4. Good English literature reading ability, at least CET-4 level

How to register/consult

"JD NLP Enterprise Project Practical Training Camp" focuses on training the top 10% NLP engineers in the industry.

Students who are interested in the course can scan the QR code to add the course consultant’s WeChat account to register and consult about the course.