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I hear people recommend textbooks a lot, and I honestly don't know why. Very few researchers I know learned machine learning through reading a textbook on their

I hear people recommend textbooks a lot, and I honestly don't know why. Very few researchers I know learned machine learning through reading a textbook on their own. Furthermore, the first two textbooks are closer to reference books rather than actual pedagogical tutorials (I haven't read any single chapter in its entirety). The Deep Learning book assumes machine learning knowledge.

Wazzymandias 10y ago
What would you recommend instead for a beginner trying to get into ML?
- cr0sh 10y ago
  I'll tell you how I started my journey: I took the Stanford ML Class in 2011 taught by Andrew Ng; ultimately, Coursera was born from it, and you can still find that class in their offerings: https://www.coursera.org/learn/machine-learning https://www.coursera.org/learn/machine-learning On a similar note, Udacity sprung up from the AI Class that ran at the same time (taught by Peter Norvig and Sebastian Thrun); Udacity has since added the class to their lineup (though at the time, they had trouble doing this - and so spawned the CS373 course): https://www.udacity.com/course/intro-to-artificial-intelligence--cs271 https://www.udacity.com/course/intro-to-artificial-intellige... https://www.udacity.com/course/artificial-intelligence-for-robotics--cs373 https://www.udacity.com/course/artificial-intelligence-for-r... I took the CS373 course later in 2012 (I had started the AI Class, but had to drop out due to personal issues at the time). Today I am currently taking Udacity's "Self-Driving Car Engineer" nanodegree program. But it all started with the ML Class. Prior to that, I had played around with things on my own, but nothing really made a whole lot of sense for me, because I lacked some of the basic insights, which the ML Class course gave to me. Primarily - and these are key (and if you don't have an idea about them, then you should study them first): 1. Machine learning uses a lot of tools based on and around probabilities and statistics. 2. Machine learning uses a good amount of linear algebra 3. Neural networks use a lot of matrix math (which is why they can be fast and scale - especially with GPUs and other multi-core systems) 4. If you want to go beyond the "black box" aspect of machine learning - brush up on your calculus (mainly derivatives). That last one is what I am currently struggling with and working through; while the course I am taking currently isn't stressing this part, I want to know more about what is going on "under the hood" so to speak. Right now, we are neck deep into learning TensorFlow (with Python); TensorFlow actually makes things pretty simple to create neural networks, but having the understanding of how forward and back-prop works (because in the ML Class we had to implement this using Octave - we didn't use a library) has been extremely helpful. Did I find the ML Class difficult? Yeah - I did. I hadn't touched linear algebra in 20+ years when I took the course, and I certainly hadn't any skills in probabilities (so, Kahn Academy and the like to the rescue). Even now, while things are a bit easier, I am still finding certain tasks and such challenging in this nanodegree course. But then, if you aren't challenged, you aren't learning.
  - Wazzymandias 10y ago
    Thank you for the comprehensive response! I have a lot to learn but it's exciting.

Wazzymandias 10y ago

What would you recommend instead for a beginner trying to get into ML?

cr0sh 10y ago
I'll tell you how I started my journey: I took the Stanford ML Class in 2011 taught by Andrew Ng; ultimately, Coursera was born from it, and you can still find that class in their offerings: https://www.coursera.org/learn/machine-learning https://www.coursera.org/learn/machine-learning On a similar note, Udacity sprung up from the AI Class that ran at the same time (taught by Peter Norvig and Sebastian Thrun); Udacity has since added the class to their lineup (though at the time, they had trouble doing this - and so spawned the CS373 course): https://www.udacity.com/course/intro-to-artificial-intelligence--cs271 https://www.udacity.com/course/intro-to-artificial-intellige... https://www.udacity.com/course/artificial-intelligence-for-robotics--cs373 https://www.udacity.com/course/artificial-intelligence-for-r... I took the CS373 course later in 2012 (I had started the AI Class, but had to drop out due to personal issues at the time). Today I am currently taking Udacity's "Self-Driving Car Engineer" nanodegree program. But it all started with the ML Class. Prior to that, I had played around with things on my own, but nothing really made a whole lot of sense for me, because I lacked some of the basic insights, which the ML Class course gave to me. Primarily - and these are key (and if you don't have an idea about them, then you should study them first): 1. Machine learning uses a lot of tools based on and around probabilities and statistics. 2. Machine learning uses a good amount of linear algebra 3. Neural networks use a lot of matrix math (which is why they can be fast and scale - especially with GPUs and other multi-core systems) 4. If you want to go beyond the "black box" aspect of machine learning - brush up on your calculus (mainly derivatives). That last one is what I am currently struggling with and working through; while the course I am taking currently isn't stressing this part, I want to know more about what is going on "under the hood" so to speak. Right now, we are neck deep into learning TensorFlow (with Python); TensorFlow actually makes things pretty simple to create neural networks, but having the understanding of how forward and back-prop works (because in the ML Class we had to implement this using Octave - we didn't use a library) has been extremely helpful. Did I find the ML Class difficult? Yeah - I did. I hadn't touched linear algebra in 20+ years when I took the course, and I certainly hadn't any skills in probabilities (so, Kahn Academy and the like to the rescue). Even now, while things are a bit easier, I am still finding certain tasks and such challenging in this nanodegree course. But then, if you aren't challenged, you aren't learning.
- Wazzymandias 10y ago
  Thank you for the comprehensive response! I have a lot to learn but it's exciting.