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From mechanical engineers to data analysts to algorithmic engineers

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In my last article, I have detailed my experience of moving from a mechanical engineer to a data analyst.


S: How do I go from Data Analyst Mechanical Engineer zhuanlan.zhihu.com

Some time ago, I moved from the data analyst to the position of the algorithm engineer. I always wanted to record the process of my own transition. I have no time and mood to write. A few days ago, I just chatted with my sister about this matter. I would like to summarize my work and study for the past six months.


In July of last year, I participated in the first hackthon competition within the company. At that time, our topic was the CTR prediction about the behavior of customer clicks. Although this competition project lasted for a short time, I learned a lot from it. Thank you colleagues for taking me a lot.


The follow-up project that I contacted at work made me interested in the natural language processing. At that time, I still asked a question, thank you for answering the answer and gave me a lot of help.

What kind of experience is the NLP algorithm engineer and what is the daily work? www.zhihu.com


First go to the knowledge route you learned.


At that time, it was just the time to catch up with Andrew Ng's deep learning course (highly recommended, it is Andrew Ng, it is easy to understand a lot of things), after the completion of this course, the principle and basic application of deep learning A general understanding. In addition, the work involved in the text classification work, I just want to implement all kinds of algorithms to see how the effect is, thanks to the department's support for me, let me apply what I have learned.


After learning the course of Andrew Ng, I went to Stanford's CS224N (thanks to the resources of Station B). This course is actually more about the application of deep learning in the NLP direction. The current development of NLP has a general concept. I followed the Data Mining course at the University of Illinois at Urbana-Champaign on coursera, which is very detailed on some of the basic knowledge of natural language processing.


In fact, the video aspect was mainly seen at the time, and the books were more theoretical books. In addition to the one-handed indow feeloow's flower book called Deep Learning, and Zong Chengqing's statistical natural language processing (but as a reference book).


In addition to these aspects, some basic knowledge of computer science has been complemented by data structures and traditional algorithms. Leetcode is a good helper in this regard. Although it hasn't brushed a lot of questions, it will continue to stick to it slowly.


I hope that everyone can go on the road of their choice.

(Continue to the Wang Wang Building) I feel like writing the same as the running account. I want to pat... Seeing that there are so many words in the words and so on.