These days, I accidentally saw the learning course of artificial intelligence on B station, and it was a dark horse (not to advertise oh). The little sister’s voice was very Nice, so I finished reading it like this, great!

Direction of artificial intelligence is the advanced direction of Python, worth learning, the streets of artificial intelligence, machine learning, neural networks what everyone see, this shows that what, illustrate the traditional universities, large companies in Chinese cabbage, since like millet into smartphones, smart phone that the price of all the way down. Don’t hesitate, if you don’t do it, it’s worth at least taking a basic course, learning more about it, and who knows when it’s going to be relevant to you

I’m in android development, machine learning, has been power on mobile terminal equipment, at least, is worth me to learn, also learn a foreign knowledge is always a good, vision to expand a lot, with not much time, actually have ready-made courses, like dark horse B some video on the site, X 20 fast one can buy a full set of video on package, Aspirants can also go to personally off-site learning, many ways, learning cost is not large, so we do not hesitate. Experience Java, Koltin, data learning, everyone should learn how to learn new knowledge, otherwise not a qualified programmer oh ~

The history of artificial intelligence

Artificial intelligence (AI), machine learning (MACHINE learning) and deep learning (DEEP learning) are three different stages of ai development

Look at this chart:

Artificial intelligence is wrapped around machine learning, and machine learning is wrapped around deep learning, and they are constantly evolving features in the book of ai skills

The Chinese say that only by taking history as a mirror can the world be governed. Learning is the same. Only by understanding the historical development context can we know where the leading technological advantage is and truly understand what is happening now

Timeline:

  • 1956.8Dartmouth College, a bunch of pundits sat down and came up withArtificial intelligence (ai)This concept, 1956 is also known as the year of artificial intelligence, before the concept of artificial intelligence came into being, it wasThe cognitive sciencePart of this time, the concept of artificial intelligence was formally established, appeared, represented by the early news: the robot that beat the chess masters, this was the early application of artificial intelligence
  • Then two branches of the technology tree of ARTIFICIAL intelligence emerged, forming two schools:
    • Link,– The idea is to imitate the biological structure of human neural network. If the machine also has the same brain neural structure as human, can it produce intelligence? It is a kind of idea of bionics
    • Logic,– also known asSymbols,“, this school believes that there is a certain logical reasoning relationship between people’s thinking. If this logic and reasoning process can be summarized, extracted and restored, is it possible to realize the same thinking?Logic,The early results were:General machine, can automatically realize the derivation of mathematical formulas
  • In the 80 s– Some people are beginning to say:Implement artificial intelligence in a statistical wayThe concept of machine learning began to emerge. At that time, the most typical application is the filtering of spam, it can be seen that the foreign countries have begun to information, we really come after ah
  • In 2010,– Since machine learning came along, people have been aligning and improving,Artificial network nerveTechnology began to emerge. Randomness was bornDeep learningThe concept. Ten years?Deep learningIn image processing, and then deep learning took off and spread to all kinds of industries

In general:

  • Machine learning is one way to achieve artificial intelligence
  • Deep learning is a further development of machine learning

Machine learning applications

Machine learning applications are now being used in a variety of industries: healthcare, aviation, logistics, education, e-commerce, etc., but generally speaking, machine learning applications are in three major fields, see the chart:

  • Image recognition– Face recognition, IDENTIFICATION, street traffic Peugeot recognition (driverless basis)
  • Traditional forecasting– Store sales, quantitative investment (financial sector), advertising recommendation, corporate customer classification
  • Natural language processing– Text classification, sentiment analysis, automatic chat, robot writing news (weather forecast, SMS news robots can automatically write), translation, intelligent customer service

Nowadays, the application exploration between machine learning, front-end and mobile devices is also in full swing. For example, the default search term on the X-Treasure search bar is to guess the application you like on mobile devices. Surprisingly, it is very accurate. The trend of 5G era is: to go cloud, part of the cloud work is done on the device, now the trend is to first process on the mobile app, and then send the data back to the server

Learning materials

1. A full set of resources for getting started – There are a lot of things you can do to get started on machine learning, including the basics of Python. It’s not realistic to spend a lot of time trying to find them yourself. But obviously the dark horse is a paid course, so I’m going to go on and on about the sources

  • The most convenient is X bao buy, 20 yuan can buy the whole set
  • Secondly, there are some video resources on station B, which is what I watched at the beginning. Students who are ok with Python can watch them directly:
    • Ai & Machine learning Quickstart 1/3
    • Ai & Machine Learning Quickstart 2/3
    • Ai & Machine Learning Quickstart 3/3
    • Dark horse B station account under the video, also part

2. Blog account: B station, headlines now many N people:

  • B station: – Dark horse programmer | Self-made millionaire
  • Headline:

3. Machine learning orientation – B station has a little brother said good, we must see

  • Position comparison: Algorithm direction & Data Analysis & Data warehouse development

4. Old Drivers on Machine learning — Listen to what old drivers have to say to our kids:

  • This is a very N little sister:How to learn ARTIFICIAL intelligence? Listen to the beautiful female algorithm engineers tell you six suggestions for learning ARTIFICIAL intelligence AI and introduction to AI booksLittle sister introduced some books:
    • Introduction to data mining(The most basic book)
    • Machine learning(The picture below the cover is watermelon, commonly known as watermelon book, the introduction of the theory is more detailed)
    • Professor Li Hang - Statistical learning methods(Statistics study, really in-depth study friends see)
    • Pattern Recognition and Machine learing(speaking of the classical algorithm, derivation is very detailed, is English, Chinese should also have, in-depth learning partners will see)
    • Cheng qing Zong - Statistical natural language processing(Choose natural language)
    • Deep learning(There are many flowers on the cover, commonly known as “Flower book”. The first 12 chapters are the theoretical basis of deep learning. Look at the friends who advance in deep learning.)

The last

Finally, I said once, didn’t you think so tall artificial intelligence, he is from statistically, we now learn is based on statistical way of thinking on the development of machine learning and joined the depth of the concept of neural network learning, in fact, the core is the study of various algorithms, the professor you in the future if you don’t do algorithm research, It’s the same as using open source libraries and apis. For Python, it’s just a few lines of code. It’s easy to learn, but it’s hard to understand