Ask HN: How to Seriously Start with Machine Learning and AI • Hacker News Board

Re: Ask HN: How to Seriously Start with Machine Learning and AI

#21

Post by throwawaymanbot » Wed, Jan 17, 2018, 3:58 PM UTC

enraged_camel wrote: post #20

atg_abhishek wrote: post #9

I'm surprised that no one has yet mentioned Andrew Ng's Machine Learning course on Coursera and to go a bit deeper, his deep learning specialization on Coursera as well. Along with the programming assignments it's a solid way to get your feet wet. And definitely second the suggestion of the fast.ai courses as well

I took this course. Andrew is a great teacher. However, I wish he worked on his public speaking a bit. He has certain speech patterns, like starting sentences with "it turns out..." and after a while it becomes extremely irritating, at least to me. Fantastic course though.

wow... nitpick....

Re: Ask HN: How to Seriously Start with Machine Learning and AI

#22

Post by pj_mukh » Wed, Jan 17, 2018, 4:04 PM UTC

Okvivi wrote: post #17

atg_abhishek wrote: post #9

I'm surprised that no one has yet mentioned Andrew Ng's Machine Learning course on Coursera and to go a bit deeper, his deep learning specialization on Coursera as well. Along with the programming assignments it's a solid way to get your feet wet. And definitely second the suggestion of the fast.ai courses as well

As somebody that has started learning AI/ML and was looking at the exact question here three months ago - I found the Andrew Ng deeplearning.ai Coursera course an amazing starting point. It was high level enough, and got me to understand enough, to get me to a point where I could start trying to build a side-project, without exposing me to the deeper math behind Neural Nets. It was a great starting point without bein…

On that note, do any of these courses take a deep dive on the deeper mathematics of neural nets?

Re: Ask HN: How to Seriously Start with Machine Learning and AI

#23

Post by AJ007 » Wed, Jan 17, 2018, 4:10 PM UTC

I wouldn't dive in to a full machine learning course as the first step.

1. Watch short tutorials about TensorFlow on Youtube

2. Look for lectures about specific topics that sound interesting.

3. Read NIPS papers that sound interesting

4. Check out the Deep Learning textbook, but maybe don't read the whole thing ( http://www.deeplearningbook.org/ )

5. By this point you should have a very rough idea about the current and past states of machine learning. You didn't have to spend any money or put in any exhaustive mental effort. If it still sounds interesting and you are motivated you can try a full online course or buy some paid books.

Re: Ask HN: How to Seriously Start with Machine Learning and AI

#24

Post by tom_wilde » Wed, Jan 17, 2018, 4:15 PM UTC

pj_mukh wrote: post #22

Okvivi wrote: post #17

Earlier quoted context omitted.

As somebody that has started learning AI/ML and was looking at the exact question here three months ago - I found the Andrew Ng deeplearning.ai Coursera course an amazing starting point. It was high level enough, and got me to understand enough, to get me to a point where I could start trying to build a side-project, without exposing me to the deeper math behind Neural Nets. It was a great starting point without bein…

On that note, do any of these courses take a deep dive on the deeper mathematics of neural nets?

Yes, see: https://www.coursera.org/learn/machine-learning

It starts out reaaaally basic but give a thorough grounding of the maths and the intuition behind it.

Re: Ask HN: How to Seriously Start with Machine Learning and AI

#25

Post by infimum » Wed, Jan 17, 2018, 4:16 PM UTC

256 wrote: post #14

I don't know much about AI but for ML specifically Elements of Statistical Learning is fantastic. I find its explanations a lot easier to understand than other resources. I recommend you skim through it to get a taste. Additionally if you prefer lectures ETHZ has recordings of their ML class[1]. The best way to learn the details is of course to read the original papers. This is especially true for following along wit…

> Additionally if you prefer lectures ETHZ has recordings of their ML class[1].

I took that class back in 2015. I found that the lectures were sometimes hard to follow, unless you already know the concepts (which creates a bit of a catch-22). For me the most valuable moments were, when Prof. Buhmann got sidetracked by some anecdotes. Would absolutely recommend, but maybe not as a starting point.

Re: Ask HN: How to Seriously Start with Machine Learning and AI

#26

Post by jongold » Wed, Jan 17, 2018, 4:23 PM UTC

Fast.ai is the only thing I recommend

Re: Ask HN: How to Seriously Start with Machine Learning and AI

#27

Post by QasimK » Wed, Jan 17, 2018, 4:24 PM UTC

256 wrote: post #14

I don't know much about AI but for ML specifically Elements of Statistical Learning is fantastic. I find its explanations a lot easier to understand than other resources. I recommend you skim through it to get a taste. Additionally if you prefer lectures ETHZ has recordings of their ML class[1]. The best way to learn the details is of course to read the original papers. This is especially true for following along wit…

Does anyone know how Elements of Statistical Learning compares to Introduction to Statistical Learning which is also from the same authors?

Re: Ask HN: How to Seriously Start with Machine Learning and AI

#28

Post by makmanalp » Wed, Jan 17, 2018, 4:27 PM UTC

atg_abhishek wrote: post #9

I'm surprised that no one has yet mentioned Andrew Ng's Machine Learning course on Coursera and to go a bit deeper, his deep learning specialization on Coursera as well. Along with the programming assignments it's a solid way to get your feet wet. And definitely second the suggestion of the fast.ai courses as well

I think fast.ai is the more "programmer-y" do-first-learn-as-you-need approach, and Andrew Ng's is the more "math-y" learn-basics-work-your-way-up approach, and they can work well together too.

Re: Ask HN: How to Seriously Start with Machine Learning and AI

#29

Post by imh » Wed, Jan 17, 2018, 4:29 PM UTC

QasimK wrote: post #27

256 wrote: post #14

I don't know much about AI but for ML specifically Elements of Statistical Learning is fantastic. I find its explanations a lot easier to understand than other resources. I recommend you skim through it to get a taste. Additionally if you prefer lectures ETHZ has recordings of their ML class[1]. The best way to learn the details is of course to read the original papers. This is especially true for following along wit…

Does anyone know how Elements of Statistical Learning compares to Introduction to Statistical Learning which is also from the same authors?

ISLR is a simplified version of ESL.

Re: Ask HN: How to Seriously Start with Machine Learning and AI

#30

Post by BucketSort » Wed, Jan 17, 2018, 4:29 PM UTC

I'm very adamant that if you really want to be an AI researcher, it starts with the mathematics. Multi-variable calculus, linear algebra,discrete math, probability and statistics are key. The classic books and courses others have suggested are excellent starting points.

I'll also say something here that isn't established, so I may take some heat. I believe there are two main paths in AI that will eventually converge to general AI: symbolic and sub-symbolic reasoning. If you go the path of symbolic reasoning, studying functional programming, theory of computation,type theory, natural language processing/compilers are in your future. If you go the path of sub-symbolic reasoning, you will be closer to optimization methods, neural networks, etc. It really depends on what you want to do. Ex: Computer vision is all about sub-symbolic reasoning, while natural language processing is heavily about symbolic reasoning. Of course advanced applications mix both! If you want to go after general AI, you gotta figure out how to tackle both forms of reasoning.

In the end, if you are serious about being an AI researcher, you will have to be a great computer scientist and mathematician. This is why it seems so difficult to get into. "Do I focus on working on messing with libraries and algorithms or the mathematical theory? And if I do both, how!?"

It's not easy, but just start and keep going. Others have provided great advice. One of my greatest joys in life has been coming to understand the marriage of computer science and mathematics under the banner of AI. It is really something exciting worth living for.

[Never give up!](https://www.youtube.com/watch?v=KxGRhd_iWuE)

Also, I recently gave a talk on getting into AI and my view on the state of things. It also has a resources section that may be of interest. Slides: https://docs.google.com/presentation/d/1pDZLkFTFjuZzM8lIKkuC...