The current state of the NLP field and the serious challenges facing AI engineers
Over the past few years, the NLP field has witnessed rapid growth, which has driven the continued presence of NLP in the industry and the demand for relevant talents. Although NLP’s rise lagged CV’s for many years, the current momentum is unstoppable.
But let’s face it, more than 90% of NLP engineers in the industry are “unqualified”. Over the past few months, we have interviewed hundreds of engineers already working on NLP, but it is clear that most of them have a weak understanding of the depth and breadth of the technology, and are mostly stuck with existing tools such as BERT, XLNet, etc. \
We have always believed that the biggest barrier for AI talent is creativity, the ability to continue to bring more value to a changing business. However, the premise of creativity must be a deep understanding of a field and a wide range of cognition, as well as the continuous questioning of a thing such as constantly asking yourself why. \
Why use Adam on this issue and not GD or Adagrad? How should I take domain knowledge into account for a particular business scenario, with priors or with constraints? For the carpooling scenario, a set of optimization objectives is designed, but it seems to be a discrete optimization problem. How should it be solved? For dichotomies, should I choose Cross entropy or Hinge Loss? BERT model is too big, and the effect is not so good. For example, next Sentence prediction, could you change it? Why does CRF not HMM work better on many NLP problems? Text generation isn’t great, so how can you tweak Beam Search to make it better? The training theme model is too slow. What if you modify gibbs sampling to run in a distributed environment? There are dependencies in the labels in the data sample. Can we add this information to the target function?
In addition, it is necessary to remain sensitive to cutting-edge technology, but the reality is that many people still have a hard time doing this for various reasons. With that in mind, Avaricious Academy is always at the forefront of technology to help you grow.
Why choose greedy Academy’s high-end NLP?
First of all, it is impossible to find another systematic training camp with such depth and breadth throughout the Internet, including foreign courses, so it is very scarce content from the point of view of content.
Secondly, even if there are many resources on the Internet, learning costs money, and the more in-depth the content, the harder it is to find good learning resources. If a course helps you clearly sort out your body of knowledge and explain the depth of knowledge clearly, that’s the biggest cost savings.
In addition, as an educational technology company focusing on THE FIELD of AI, the strength of the teaching and research team is very top in the industry, including the best paper authors and ALBERT authors.
Finally, our NLP Advanced Training Camp (phase 7) has made a lot of upgrades on the original basis, integrating more cutting-edge content, and deepening the difficulty of some content.
Specially designed for AI practitioners/graduate students/researchers
NLP Advanced Training Camp for Natural Language Processing
Students who are interested in the course
Add course consultant little sister wechat
Registration, course consultation
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Part 02 Project assignments
The course design closely revolves around the latest progress of academia and the needs of industry, covering all the core knowledge points, and combined with a large number of practical projects, to cultivate students’ hands-on ability and problem-solving ability.
03 Live lecture, live derivation demonstration
Different from the poor PPT explanation, the tutor deduces the whole process on site, allowing you to have a clear idea in learning and a profound understanding of every detail of the derivation behind the algorithm model. More importantly, you can clearly see the relationships between the various models! Help you get through six pulses! \
▲ From: CRF and Log-Linear model explanation
▲ From: CRF and Log-Linear model explanation
▲ from: Convex Optimization
▲ from: Convergence Analysis
No matter how many obstacles you encounter in your learning process, you can solve them in four ways:
1. Ask the tutor directly online;
2. Record it in the shared document and answer questions live at fixed time every day;
3. Full-time teaching assistants in the learning community, ready to ask questions
4. Common problems will be explained in Review Session
Note: The class teacher will record every question and answer for students to check in real time.
Weekly course arrangement
Live teaching is adopted, including 4 to 5 live lectures a week, including 2 main lectures, 1 to 2 Discussion session (explaining a certain actual practice, essential basis, case or extension of technology). 1 paper Reading session (a necessary paper will be assigned every week, and the interpretation will be live). The teaching model also refers to the teaching system of America’s top universities. The following is one week’s course schedule for your reference.
Your essential challenge
1. Write technical articles
By publishing relevant technical articles on Zhihu to test my own achievements, it is also a way of collision of ideas. The tutor will write a detailed comment on each article published. What if you accidentally become a big V? Although the process of writing the article is extremely painful, the study group in the middle of the night howling, but look at the article written by holding the hair is very happy! Looking at the number of thumbs up, we all silently thanked our mentors for their ruthlessness!
This kind of full sense of achievement, let everyone write one after another!
2
In addition to the article, the algorithm engineer’s foundation — project code, the tutor will not let go. For each assignment in Gitlab, tutors will lead a team of teaching assistants to give detailed correction and feedback. And force you to constantly optimize!
06 Curriculum Development team
Greedy institute jointly by Google, amazon, Microsoft and other companies 11 AI AI scientists constantly grinding refinement of course content, course basis part covers the AI technology neighborhood practitioners must all core knowledge, at the same time joined the latest academic study on the depth of the course teaching and new developments of the industry, Ensure that students learn popular AI knowledge and skills in domestic and foreign enterprises.
▲ Brief introduction of some course R&D tutors
These two days the group is even more successful. In the first three phases of our program, many students have been admitted by first-line AI enterprises, and others are waiting for offers through the second and third phases. We are sure to receive more good news in the coming weeks!
Cut off some random student feedback.
I made sure that our devil camp did not mislead anyone, and that our courses really helped people to improve their skills or get an offer.
This time we are welcoming the 8th NLP enrollment, do not think of this as a normal training camp for other online courses. Due to the professionalism and depth of the content, it has attracted a large number of students from the world’s top universities in the past, including Stanford, UCSD, USC, Columbia University, HKUST, Edinburgh and so on. Here, you can not only enjoy access to top talent, but also meet like-minded AI practitioners and future scientists.
07 Registration Instructions
1. This course is fee-based.
2. Only 34 students will be enrolled for this period.
3, quality assurance! The full refund is unconditional within 7 days after the official start of the course.
4. Learning this course requires a certain AI foundation.
Low low low
Specially designed for AI practitioners/graduate students/researchers
NLP Advanced Training Camp for Natural Language Processing
Students who are interested in the course
Add course consultant little sister wechat
Registration, course consultation
????????????????????