I'm writing these two days 2017 Annual work summary , What I learned in this year , A lot to say .

   Towards the end of the year , Finally, I finished learning the algorithm in Zou Bo's machine learning video , Otherwise, it's the four hundred dollars that I really need to spend . In order to understand his formula derivation , We have to put the high number , linear algebra , Probability and statistics again , Then when learning the algorithm formula derivation, we found that the derivative matrix distribution is the same as never seen before , We can only go back and nibble again . Countless times in the process, I doubted that my IQ was passed on to my son , I didn't leave anything for myself ; I've doubted many times whether I studied statistics in University , Actually t distribution F There's no impression of distribution or anything ; I doubted many times whether I had learned machine learning when I was a graduate student , At that time, Mao didn't think the least squares had anything to do with the maximum likelihood estimation ; And then there's a lot of skepticism about my memory. I really need to go back home —— The new algorithm is over. I don't remember what happened to the last one , Even if it's something I thought I understood a week ago —— Typical bear breaking stick , Um. , I can only admit that I am a bear . I'm glad I didn't have the courage to go home and be a housewife , I can only insist on being tough and cheeky , At last, I learned seven, eight, eight , As for the forgotten five five six six , You can only read your notes a few times before you forget them .

   It is strongly recommended that students who are good at mathematics in university learn machine learning , You will find that mathematics is really amazing ; It is also strongly recommended that students who are not good at mathematics in universities learn machine learning as well , And tell me how you feel , To give me some comfort , It turns out that my IQ is normal , Let me have more courage to keep going , thank you !

   In order to learn machine learning , Yes python, Although I used it before java Of weka, But now it seems that all the people use it python Do machine learning , You see, most machine learning classes use python To make code , And it's said that it can be directly used in the production environment , Of course I have to keep up with the trend , What's more, what I learned is to do projects, not research ; You see, you see python Those kits for numpy,pandas,matplotlib,statsmodels as well as sklearn Wait, wait, wait , From matrix calculation to statistics to visualization to machine learning , have everything that one expects to find , So, you have to python! It just means , I have to learn. I have to learn , My poor memory !

   Before that , Because I heard spark than hadoop Learn when you have more cows spark, For learning spark Yes scala, Online because spark yes scala Write so use scala write spark Program comparison java,python More authentic , Of course I have to learn the most authentic . I just knew I had to learn python, Why should I be so embarrassed , It's too late to cry , And now think about it , I don't remember at all scala What is it , My poor memory !

   this year , use spark+kafka+redis Made a recommendation ; Two simple models are made with random forest and autoregression ; then , No more . It's like this year , I really don't think I've done anything , I often wonder if I will be sent home because of no performance —— god bless ! ! Buddha bless !! Doom Asylum !!!
   New work plan for the new year .

   Read a Microsoft article AI Articles written by female engineers , call “ introduction AI, How to choose a down-to-earth position ”. Inside AI Position divided into Algorithm Engineer ( Data scientist ), Machine learning Engineer ( Commissioning Engineer ), Three positions of data manager . say concretely , Algorithmic engineers invent or optimize algorithms and can integrate theory with practice ; Parameter adjustment engineers use mature algorithms to optimize parameters for engineering industrialization ; Data manager , Familiar with the business, familiar with the data, able to simply analyze the data, find out the possible requirements, possible models, and coordinate the algorithm engineers or parameter engineers to complete the data products . There's a very funny analogy :
- The algorithm is to kill the dragon , Jianghu with swords , Tianwaifeixian ;
- Engineering is hunting , Galloping horse , Bingjiu Bingge ;
- Making data is raising pigs , Mix pig food and clear pig manure every day , A face of dirt and a body of mud .

   Um. , Down to earth analysis of my current situation : Don't even think about Tu Long ; The hunter will learn to shoot arrows on horseback , Maybe lucky enough to hunt a hare, a deer or something , Wolf, tiger, forget it , I'm afraid of being eaten ; I don't want to raise pigs , But no one seems to be doing it for me . therefore , Down to earth ,2018 My job in : Pig raising , Rabbit beating . Um. , also , Organize learning notes, summaries and so on into blog —— If you have time to finish housework with your child after work ……
   okay , a new year , I hope I can survive the middle age crisis , Come on !