请问我能直接从Julia开始入门机器学习吗

最近几个月总是看到一些无知媒体在吹捧python的同时顺便推销他们的课程,却有意忽略了其他新兴语言,比如Rust,Clojure,并总喜欢把python和c++,java比较,我开始对这种 语言有一点抵触情绪,但学机器学习python是必经之路,我想学好理论并用Julia做实践,我想有人为我指一条明路

  1. 我想知道机器学习的理论课程
  2. Julia中有关机器学习的包有那些
  3. 这些包的说明文档在哪里
  4. 有没有练习实例
  5. 有没有大佬分享他的作业

https://juliaacademy.com/
里面有免费课程

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sign up 按钮无反应,无法注册。

有google验证

还是学python吧,又不难

想法挺好的,还是鼓励下,不过没人交流的话,学起来还是蛮寂寞的…


这个还是满推荐的,有兴趣用julia也试试?

Before your learning of ML, one thing you need take care is to position yourself exactly. Try to understand your current background of mathematics and programming. No matter in ML or DL or any other AI algorithms, these are always very fundamental parts.
I have a simple list/roadmap for you to start machine learning.
For the theory of ML:

  1. Calculus(you may need to learn extra calculus including the derivation of vector and matrix that is relatively difficult for beginners)
  2. Advanced algebra or linear algebra
  3. Papers about support vector machine.
  4. Papers about clustering(According to your reality choose you the most popular clustering algorithms to learn first)
  5. Papers about logistical regressions.
    All above should be learnt on theory first instead of practising directly.

For the theory of DL:

  1. The basic papers about naive neural networks.
  2. Try free video lessons of Prof. Andrew Ng in NetEase open course. (you need not to care the practical part during the lesson, because they use python to illustrate. )

Finally, programming language is just a tool. We could use Java yesterday and the use c plus plus today and python for tomorrow. We need choose among languages to fit in the specific situations rather than to be bound by the language we have learnt. Just break through yourself. Study the theory well first for they will always there while any languages will change through time. After your deep knowing about ML, any language could be your partner to practice and realise some algorithms of machine learning.
Best wishes!

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