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    Computer is a very young subject, its history is only a hundred years, but computer is one of the most core subjects of this era, permeated in every aspect of our lives. In this learning part, I have a basic understanding of the subject of computer. In the aspect of application, I have learned how to use html and css[1]. 1. Preview more before class: It's a good idea to preview the course before you go to class. Then underline what you don't understand. Focus on what you don't understand in class, so it will be easier to learn. I won't be bored because I don't understand in class. Because the class is going to be fast. It's hard to follow the lesson if you don't understand something. So it's best to get into the habit of reading before class[2]. 2. Listen carefully in class: That's the basic thing. You can't understand anything unless you listen. The more you learn, the more you will not understand, the more you do not understand, the more boring you will feel, then you will certainly not learn well Do it after class: Make sure you do it. Good knowledge of computer science = good theoretical knowledge + write (correct) the code +de a bug+ finally run through. This process must be independent, never slippery, can seek advice, but must not go to the big boss directly copy their code or results, must eat all shit and finally wade through the past, fully understand what they wrote, only need to go through a few times after this hard process, [3]Basically, there will be a qualitative improvement in the code ability and professional quality. Computer major is a major that pays attention to practical east less operation. So spare time must write more code, more practice. Learn new knowledge online, because software is a very fast update industry, it may be you learn today, tomorrow will be out of date, this is normal, so to learn more about the industry.[4]

    Reference
    [1]WEI X, YU C, ZHANG P, et al. Automated Systolic Array Architecture Synthesis for High Throughput Cnn Inference on FPGAs[C]// Proceedings of the 54th Annual Design Automation Conference, 2017:1-6.
    [2] WELLER D, OBORIL F, LUKARSKI D, et al. Energy Efficient Scientific Computing on FPGAs Using OpenCL[C]// Proceedings of the 2017 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays,2017: 247-256.
    [3] PARK J, SUNG W. FPGA Based Implementation of Deep Neural Networks Using On-Chip Memory Only[C]// 2018 IEEE International conference on acoustics, speech and signal processing (ICASSP), 2018: 1011-1015.
    [4] SAK H, SENIOR A, BEAUFAYS F. Long Short-Term Memory Based Recurrent Neural Network Architectures for Large Vocabulary Speech Recognition[J]. arXiv preprint arXiv, 2014: 1402.1128.