Great talk, though since machine learning is more about data than code (7 lines of PHP code were used in the example), the whole topic felt a bit abstract and a little over my head. I did come away with a better understanding and some tools to try which will hopefully be enough to get my feet wet.
I've attended machine learning talks focused on .NET and the Azure cloud before, so I was familiar with some of the principles but the technology was lost on me. Damien finally made machine learning approachable for me, and (as I told him after the talk) he's probably started me down a long path of trying to teach machines to do cool things.
Nicely done, approachable and entertaining, and makes you realise that machine learning can be simple to implement in practise, even though how it works remains quite mysterious!
I knew nothing about machine learning or neural networks before this talk and now I know enough to get started. I still don't quite understand how the input and output files work and would have liked a little more detail on how those are formatted and utilized.
Liked this talk a lot. It focused on a single library that can be used in php to achieve machine learning. It could have been a lot higher level but I don't think that would have been as useful. Machine learning is a huge subject and hard to really scratch the surface with in an hour.
I walked in with an extremely limited knowledge of machine learning and left with enough information to get started. Damien was very animated and kept things moving along at a good pace. The examples were good and easy to follow. The slideshare french subdomain didn't work for me, but I did find the slides here: http://www.slideshare.net/dseguy/machine-learning-in-php-62394863
I've run across many references to "machine learning" and I wanted to know what it's about, and whether or not it's something I/we should explore further. It is. This talk is a great introduction with a concrete example and showing that tuning is important. This was exactly the level I needed.
Excellent talk with great use case example for the audience. Just enough to get one's feet wet in the subject and get a general understanding of machine learning.
- Didn't fully grasp all the inputs and outputs when setting up fann
- Finding commented source code was good example, but not as relevant to me. I work in ecommerce so was thinking about recommendation engine which you brought up. Wondering if you have experimented with other examples?
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Great talk, though since machine learning is more about data than code (7 lines of PHP code were used in the example), the whole topic felt a bit abstract and a little over my head. I did come away with a better understanding and some tools to try which will hopefully be enough to get my feet wet.
Really enjoyed this overview into a topic of which I have great interest.
Slides : http://fr.slideshare.net/dseguy/machine-learning-in-php-62394863
Great introduction to machine learning with a practical and accessible example.
Well paced, good interaction with audience. Real example, got the concept. Thanks!
I've attended machine learning talks focused on .NET and the Azure cloud before, so I was familiar with some of the principles but the technology was lost on me. Damien finally made machine learning approachable for me, and (as I told him after the talk) he's probably started me down a long path of trying to teach machines to do cool things.
Nicely done, approachable and entertaining, and makes you realise that machine learning can be simple to implement in practise, even though how it works remains quite mysterious!
Great intro to the topic. Thanks for sharing.
I knew nothing about machine learning or neural networks before this talk and now I know enough to get started. I still don't quite understand how the input and output files work and would have liked a little more detail on how those are formatted and utilized.
Absolutely brilliant. Every talk of Damiens that I've been to has been really great.
Really good stuff. I would have emphasized more how our machine-learned models should be quantitatively assessed (precision vs. recall, f-score, etc.)
Liked this talk a lot. It focused on a single library that can be used in php to achieve machine learning. It could have been a lot higher level but I don't think that would have been as useful. Machine learning is a huge subject and hard to really scratch the surface with in an hour.
I walked in with an extremely limited knowledge of machine learning and left with enough information to get started. Damien was very animated and kept things moving along at a good pace. The examples were good and easy to follow. The slideshare french subdomain didn't work for me, but I did find the slides here: http://www.slideshare.net/dseguy/machine-learning-in-php-62394863
I've run across many references to "machine learning" and I wanted to know what it's about, and whether or not it's something I/we should explore further. It is. This talk is a great introduction with a concrete example and showing that tuning is important. This was exactly the level I needed.
Excellent talk with great use case example for the audience. Just enough to get one's feet wet in the subject and get a general understanding of machine learning.
- Didn't fully grasp all the inputs and outputs when setting up fann
- Finding commented source code was good example, but not as relevant to me. I work in ecommerce so was thinking about recommendation engine which you brought up. Wondering if you have experimented with other examples?