4 ms·

Since there seems to be an uptick in submissions related to machine intelligence projects lately (or rather since Google released http://tensorflow.org http://t

Since there seems to be an uptick in submissions related to machine intelligence projects lately (or rather since Google released http://tensorflow.org http://tensorflow.org) (e.g. https://news.ycombinator.com/item?id=10537697 https://news.ycombinator.com/item?id=10537697) does anyone dare to make a comparison? I am currently just getting started in this area due to my interest in AI and I am not quite sure which route to choose.

mjhirn 11y ago
MJ here - founder of Autumn and creator of Leaf. I can give a quick comparison on Leaf vs. TensorFlow - although this might seem somewhat biased. We like what the Google engineers did on TensorFlow and share a lot of similar ideas on how future Machine Learning should be structured and implemented. Especially that in the end it is just a performant pipeline for numeric information processing. We feel that the biggest difference (besides the different stages of the project) is the language and the ecosystem that it embraces. We are strong believers in Rust and think that it might have good chances to succeed in the long run - which we outline in more detail in the Q&A[1]. Due to your question I would recommend picking up on the basic concepts of machine intelligence and then going with whatever ecosystem fits the task/stack. Might become similar to choices of web frameworks in the next couple of years. I think Neon[2] - as a Python framework - seems to be easy and performant as well. [1]: https://github.com/autumnai/leaf#why-rust https://github.com/autumnai/leaf#why-rust [2]: https://github.com/NervanaSystems/neon https://github.com/NervanaSystems/neon
halflings 11y ago
If you're just getting started in this area (machine learning), I'd advise you to look at simpler and more interpretable models first (things like logistic regression, decision tree, perceptron) using simple tools (scikit-learn for instance). Something like Tensor Flow has a lot of mechanics you have to understand before getting started, and it's "lower-level" than other ML libraries. And I definitely wouldn't advise you to use "deep learning" as your first ML model. It's hard to understand, and the result of training it is hard to interpret.
bjwbell 11y ago
I highly recommend Weka, http://www.cs.waikato.ac.nz/~ml/weka/ http://www.cs.waikato.ac.nz/~ml/weka/. I used it for river flow forecasting (published a paper) and it was very good. We ran about 100 different models, various neural nets, SVM, logistic regression, multilinear regression, etc. I spent about a month trying different tools and liked the understandability of Weka. Scikit-learn calls out to C for the heavy lifting, which is great but it's not exactly easy to read and understand the code.

mjhirn 11y ago

MJ here - founder of Autumn and creator of Leaf. I can give a quick comparison on Leaf vs. TensorFlow - although this might seem somewhat biased. We like what the Google engineers did on TensorFlow and share a lot of similar ideas on how future Machine Learning should be structured and implemented. Especially that in the end it is just a performant pipeline for numeric information processing. We feel that the biggest difference (besides the different stages of the project) is the language and the ecosystem that it embraces. We are strong believers in Rust and think that it might have good chances to succeed in the long run - which we outline in more detail in the Q&A[1]. Due to your question I would recommend picking up on the basic concepts of machine intelligence and then going with whatever ecosystem fits the task/stack. Might become similar to choices of web frameworks in the next couple of years. I think Neon[2] - as a Python framework - seems to be easy and performant as well. [1]: https://github.com/autumnai/leaf#why-rust https://github.com/autumnai/leaf#why-rust [2]: https://github.com/NervanaSystems/neon https://github.com/NervanaSystems/neon

halflings 11y ago

If you're just getting started in this area (machine learning), I'd advise you to look at simpler and more interpretable models first (things like logistic regression, decision tree, perceptron) using simple tools (scikit-learn for instance). Something like Tensor Flow has a lot of mechanics you have to understand before getting started, and it's "lower-level" than other ML libraries. And I definitely wouldn't advise you to use "deep learning" as your first ML model. It's hard to understand, and the result of training it is hard to interpret.

bjwbell 11y ago

I highly recommend Weka, http://www.cs.waikato.ac.nz/~ml/weka/ http://www.cs.waikato.ac.nz/~ml/weka/. I used it for river flow forecasting (published a paper) and it was very good. We ran about 100 different models, various neural nets, SVM, logistic regression, multilinear regression, etc. I spent about a month trying different tools and liked the understandability of Weka. Scikit-learn calls out to C for the heavy lifting, which is great but it's not exactly easy to read and understand the code.