Hello
I was using https://membrain-nn.de/ as a starting point.
MemBrain is a powerful graphical neural network editor and simulator for Microsoft Windows, supporting artificial neural networks of arbitrary size and architecture.
Hope that helps
Best regards
Gianni
Machine Learning Crash Course
Hi,
There's one that's more introductory on MIT opencourseware
https://ocw.mit.edu/courses/electrical- ... fall-2016/
There's a course on coursera by Andrew Ng. It's pretty good.
Someone recently recommended fast.ai to me, haven't delved into it yet but looks pretty good.
There's one that's more introductory on MIT opencourseware
https://ocw.mit.edu/courses/electrical- ... fall-2016/
There's a course on coursera by Andrew Ng. It's pretty good.
Someone recently recommended fast.ai to me, haven't delved into it yet but looks pretty good.
https://developers.google.com/machine-l ... sh-course/ankitdixit wrote: ↑Fri Jun 28, 2019 7:08 amHello All, Anyone suggests me a way to learn machine learning crash course or any other way to learn machine learning. Any online resources?
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firlandsfarm
- Posts: 2720
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There is a free course on the Harvard website … https://online-learning.harvard.edu/cou ... ng?delta=0
Can't vouch for how good it is but it is Harvard! (Unless it's a fake website.)
Can't vouch for how good it is but it is Harvard! (Unless it's a fake website.)
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ShaunWhite
- Posts: 9731
- Joined: Sat Sep 03, 2016 3:42 am
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firlandsfarm
- Posts: 2720
- Joined: Sat May 03, 2014 8:20 am
I forgot to paste the MIT courses (Massachusetts Institute of Technology) which are also free … https://ocw.mit.edu/search/ocwsearch.ht ... 20learning. They are also world renown.firlandsfarm wrote: ↑Sat Jun 29, 2019 12:54 pmThere is a free course on the Harvard website … https://online-learning.harvard.edu/cou ... ng?delta=0
Can't vouch for how good it is but it is Harvard! (Unless it's a fake website.)
STOP PRESS: sorry the original link seemed to stop working, have posted a corrected link in it's place. Should work now.
Install the latest version of MRAN (the Microsoft release of R), then download R Studio - preferably on Linux but windows is also ok. Then follow a turorial on whatever type of problem you are trying to solve i.e. regression/classification etc.
Then install a library called 'caret' and you can do pretty much any machine learning algo you want, it also has functions for pre-processing, feature selection. You can easily tune the parameters for the algo's too.
Then install a library called 'caret' and you can do pretty much any machine learning algo you want, it also has functions for pre-processing, feature selection. You can easily tune the parameters for the algo's too.
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firlandsfarm
- Posts: 2720
- Joined: Sat May 03, 2014 8:20 am
OK perhaps not Machine Learning but not wishing to create a new thread I am posting this here as a heads-up. Following-on from my previous post I have just received an email from Harvard announcing a new course ... "Fat Chance: Probability from the Ground Up" and thought it may be of interest to some members. The course is online and free, details here (https://www.edx.org/course/fat-chance-p ... round-up-2). The course is expected to last 7 weeks and require 3-5 hours per week. I have not looked into the detail but it looks like it starts from the bottom but at the top (Module 7) could add to the thinking of even experienced campaigners. The course is described in the introduction as ...
"In Modules 1 and 2, you will be introduced to basic counting skills that you will build upon throughout the course. In Module 3, you will apply those skills to simple problems in probability. In Modules 4 through 6, you will explore how those ideas and techniques can be adapted to answer a greater range of probability problems. Lastly, in Module 7, you will be introduced to statistics through the notion of expected value, variance, and the normal distribution. You will see how to use these ideas to approximate probabilities in situations where it is difficult to calculate their exact values."
"In Modules 1 and 2, you will be introduced to basic counting skills that you will build upon throughout the course. In Module 3, you will apply those skills to simple problems in probability. In Modules 4 through 6, you will explore how those ideas and techniques can be adapted to answer a greater range of probability problems. Lastly, in Module 7, you will be introduced to statistics through the notion of expected value, variance, and the normal distribution. You will see how to use these ideas to approximate probabilities in situations where it is difficult to calculate their exact values."