As a small editor working in the technology industry, in order not to be disliked by our boss on this road, we must try our best to learn a series of skills that may be used in the future to arm ourselves. At this stage in the field of artificial intelligence, neural networks, machine learning, and deep learning are flooding our lives. If you don’t understand one or two, it’s really embarrassing to say that you are a person in this industry…
However, in the process of self-study, there are always various difficulties, such as learning from scratch, I don't know where to start, I found this textbook and found that I couldn't understand the second chapter ; lack of basic knowledge, Stuck on a "common sense" problem that some tutorials skip; having a problem and don't know how to find a solution ....
How is Xiaobai going to teach himself? Don't worry, after reading the information sent by the editor today, all these questions will be solved!
The materials that the editor is going to send today include three parts. The first part is carefully prepared for friends who are not well-founded in mathematics . After all, to learn the theory of machine learning well, the basics of mathematics must be passed, and the probability theory of high number line generation must be at least known. Well, otherwise you will be really confused when you learn the theory~
The second part is prepared for friends who are not very good in python, or who are new to python . If you feel that you are already familiar with python, you can skip this part.
The third part is the content of deep neural networks. Everything you want to know about deep neural networks can be learned from it . It is worth mentioning that there is also a machine learning tutorial of matlab, which is absolutely dry!
Whether you are a novice or have a certain foundation, this set of materials is very good! There are a lot of video teaching in the materials. Compared with reading books, the biggest advantage of video teaching is that many lecturers have done many projects in the field of machine learning or data mining. Experienced people will teach many things that they cannot learn by reading books. , useful experience. So friends who have taken the materials can study hard.
Let's take a look at the content of the data
Part 1, Mathematical Fundamentals (10 hours in total)
The second part, python basics and data analysis
1.python基础篇教程（22课）
2.python入门与进阶
第三部分，深度学习算法与编程实践
1.编程入门
（1）matlab与机器学习入门 进阶和提高 （共13课时，内附练习、matlab代码和算法包，还有ppt课件）
（2）深度学习 tensorflow教程（是莫烦的教学视频，最大特点是简单易懂）
2.基础精讲
（1）原理精讲班 （共12课时）
（2）基础一（个人觉得挺不错的 ，讲的很详细）
（3）基础二（紧接着基础一）
3.深度学习
（1）深度神经网络深入研究 （共20课时，内附ppt课件）
（2）深度神经网络深入与强化 （共10课时，内附ppt课件）
看完资料介绍，有没有一种超级想要学习的冲动？？？在大数据和人工智能发展那么迅猛的今天，怎么能不武装一下自己呢？
资料太多，所以小编已经打包上传至网盘了，下载链接：https://pan.baidu.com/s/1wS3XPP33wSATlf940g3C9g 密码：1dcw（如链接失效，请前往公众号下载：OFweek人工智能）