A Tour of the Top Algorithms for Machine Learning Newbies
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A Tour of the Top Algorithms for Machine Learning Newbies
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Re: A Tour of the Top Algorithms for Machine Learning Newbies
#2I'm a machine learning newbie, and I'd really like to speak with someone in the field to figure out what I'm wanting to learn.
The problem I have is that I have a large labeled data set of spoken words and phonemes from a single speaker. I'd like to train a model and generate new phonemes (of various pitches, speeds, and intonations) with which to build a concatenative speech engine.
What algorithms and models would I be looking to use? What are the primary techniques? Is this something I could quickly begin to see results in, or would it take months or years of tweaking?
To clarify what I'm doing, I own/built trumped.com, and I'm trying to improve the speech synthesis quality by generating better fitting units of speech.
Re: A Tour of the Top Algorithms for Machine Learning Newbies
#3If you can't explain something in a different way from how you learned it, you probably don't understand it adequately.
Re: A Tour of the Top Algorithms for Machine Learning Newbies
#4Nice to see a roundup of ML that doesn't just go straight to Deep Learning for a change :-)
Re: A Tour of the Top Algorithms for Machine Learning Newbies
#5The image for logistic regression is hilariously wrong. It shows the sigmoid as a decision boundary.
Also dont get hung up about no free lunch theorem. That is a great result in computer science theory with little practical impact: just pick neural networks for unstructured and GBDT for structured data. For the vast majority of real-life problems (not all possible problems) these are the single best algorithms.
Re: A Tour of the Top Algorithms for Machine Learning Newbies
#6The image for logistic regression is hilariously wrong. It shows the sigmoid as a decision boundary. Also dont get hung up about no free lunch theorem. That is a great result in computer science theory with little practical impact: just pick neural networks for unstructured and GBDT for structured data. For the vast majority of real-life problems (not all possible problems) these are the single best algorithms.
[deleted]
Re: A Tour of the Top Algorithms for Machine Learning Newbies
#7The image for logistic regression is hilariously wrong. It shows the sigmoid as a decision boundary. Also dont get hung up about no free lunch theorem. That is a great result in computer science theory with little practical impact: just pick neural networks for unstructured and GBDT for structured data. For the vast majority of real-life problems (not all possible problems) these are the single best algorithms.
> just pick neural networks for unstructured data
... except that NNs require a large amount of training data.
Re: A Tour of the Top Algorithms for Machine Learning Newbies
#8No mention of Deep Learning in a top ML algorithm list in 2018? Kinda odd if you ask me.
Also, in the SVM section, no mention of kernel methods? (yet the picture shows a windy boundary). Also odd.
Re: A Tour of the Top Algorithms for Machine Learning Newbies
#9Nice to see a roundup of ML that doesn't just go straight to Deep Learning for a change :-)
Or at all, for that matter.
Re: A Tour of the Top Algorithms for Machine Learning Newbies
#10The image for logistic regression is hilariously wrong. It shows the sigmoid as a decision boundary. Also dont get hung up about no free lunch theorem. That is a great result in computer science theory with little practical impact: just pick neural networks for unstructured and GBDT for structured data. For the vast majority of real-life problems (not all possible problems) these are the single best algorithms.
Logistic regression isn't sexy, but it can still achieve near state-of-the-art results, is reasonably resistant to bias^H^H^H^H variance, and generates parameters that you can easily explain to someone with no background in math.
There's a lot of value in all that. Especially if your deliverable is something that a business is going to use, and not just a Kaggle entry.