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If you want to jump right in with "hello world" type TensorFlow (a tool for machine learning), see https://news.ycombinator.com/item?id=12465935 https://news.yc

by jotto 10y ago

If you want to jump right in with "hello world" type TensorFlow (a tool for machine learning), see https://news.ycombinator.com/item?id=12465935 https://news.ycombinator.com/item?id=12465935 (how to fit a straight line using TensorFlow) If you like to study/read: the famous Coursera Andrew Ng machine learning course: https://www.coursera.org/learn/machine-learning https://www.coursera.org/learn/machine-learning If you just want course materials from UC Berkeley, here's their 101 course: https://news.ycombinator.com/item?id=11897766 https://news.ycombinator.com/item?id=11897766 If you want a web based intro to a "simpler" machine learning approach, "decision trees": https://news.ycombinator.com/item?id=12609822 https://news.ycombinator.com/item?id=12609822 Here's a list of top "deep learning" projects on Github and great HN commentary on some tips on getting started: https://news.ycombinator.com/item?id=12266623 https://news.ycombinator.com/item?id=12266623 If you just want a high level overview: https://medium.com/@ageitgey/machine-learning-is-fun-80ea3ec3c471 https://medium.com/@ageitgey/machine-learning-is-fun-80ea3ec...

kevindeasis 10y ago
I don't know anything about tensor flow except the very tip of the iceberg. Can you know nothing about ml, ai, data analysis, and stats then give tensor flow some input and it will give you some input and pretty much apply it to your app? Or do you have to know these subjects before even starting tensor flow?
- jotto 10y ago
  Yes, you can use TensorFlow directly and not know much, here are more examples https://github.com/aymericdamien/TensorFlow-Examples https://github.com/aymericdamien/TensorFlow-Examples It's OK to jump in and try it without having background information. See how far you get and start researching when you hit a wall or find sudden interest.
  - kevindeasis 10y ago
    Brilliant! Thanks for the link
allenleein 10y ago
Oh btw, I think it's too annoying to 'follow the pace' on Moocs so I recommend you can download all the courses right here: http://academictorrents.com/browse.php?search=machine+learning http://academictorrents.com/browse.php?search=machine+learni...
gcr 10y ago
I actually recommend jumping right into the excellent Scikit-learn tutorials, http://scikit-learn.org/stable/tutorial/ http://scikit-learn.org/stable/tutorial/ Unlike some of the other complicated tools, sklearn is just a "pip install" away and includes all sorts of examples of different problems. Classification? Regression? Clustering? Representation learning? Perceptual embedding? Odds are, some part of sklearn covers all of that.
- wooter 10y ago
  having done ML R&D for a few years, they're docs are great for orienting newcomers to the field
- geebee 10y ago
  The scikit-learn tutorials are great. Another nice thing about scikit-learn is that the api for a lot of different ML algorithms is very similar, almost identical. This means that you can set up a train and and test set and swap in and out random forest, svm, naive bases, logistic regression, and various others. Read about them one by one, try to understand the algorithms generally, test them out, see how they perform differently on different data sets. It all depends on how you like to approach a new subject, but I think this is more fun and motivating than going straight into the mathematics behind the algorithms right away (which is more along the lines Andrew Ng's excellent course). I'd say once you're into it and using the algorithms, then dig deeper into the core mathematics, you'll have a better context for it.
sanderjd 10y ago
What is a good "hello world" project for machine learning? That is, what problem can I solve or question can I answer with minimal ceremony, and ideally with multiple techniques / technologies so that I can compare them? Is it this house price estimation like in your last link, or is there something better than that?
- tsm 10y ago
  The Iris data set [1] is very famous and a popular way to test out classification techniques. It's not "big data", but can be used to familiarize yourself with some basic data mining techniques. [1] – https://en.wikipedia.org/wiki/Iris_flower_data_set https://en.wikipedia.org/wiki/Iris_flower_data_set
- sgk284 10y ago
  Kaggle has a number of starter challenges. See https://www.kaggle.com/c/titanic https://www.kaggle.com/c/titanic for one related to predicting the survival of passengers on the Titanic.
  - sanderjd 10y ago
    Nice, that is a great pointer.
  - dschiptsov 10y ago
    Lol. Predicting the survival of passengers on the Titanic is meaningless and misleading - there is literally no connection to reality, despite the framing of the task which suggests a certain connection. There is absolutely nothing that could be predicted. It is just a simulation of oversimplified model which describes nothing, but an oversimplified view of a historical event. It is as meaningless as the ant simulator written by Rich Hickey to demonstrate features of Clojure - it has that much connection to real ants.
    - edgyswingset 10y ago
      Huh? Why would a connection to reality be required to get started with machine learning?
highd 10y ago
I would be pretty hesitant to start talking about TensorFlow and Deep Learning before confirming, for example, at least a rudimentary understanding of Linear Algebra.
akhilcacharya 10y ago
Regarding deep learning, what are some resources for learning strategies about improving network architectures? I read all of these architectures in research papers, but I'd really love to learn how to start iterating on them for a particular domain.

by jotto 10y ago

kevindeasis 10y ago

I don't know anything about tensor flow except the very tip of the iceberg. Can you know nothing about ml, ai, data analysis, and stats then give tensor flow some input and it will give you some input and pretty much apply it to your app? Or do you have to know these subjects before even starting tensor flow?

jotto 10y ago
Yes, you can use TensorFlow directly and not know much, here are more examples https://github.com/aymericdamien/TensorFlow-Examples https://github.com/aymericdamien/TensorFlow-Examples It's OK to jump in and try it without having background information. See how far you get and start researching when you hit a wall or find sudden interest.
- kevindeasis 10y ago
  Brilliant! Thanks for the link

allenleein 10y ago

Oh btw, I think it's too annoying to 'follow the pace' on Moocs so I recommend you can download all the courses right here: http://academictorrents.com/browse.php?search=machine+learning http://academictorrents.com/browse.php?search=machine+learni...

gcr 10y ago

I actually recommend jumping right into the excellent Scikit-learn tutorials, http://scikit-learn.org/stable/tutorial/ http://scikit-learn.org/stable/tutorial/ Unlike some of the other complicated tools, sklearn is just a "pip install" away and includes all sorts of examples of different problems. Classification? Regression? Clustering? Representation learning? Perceptual embedding? Odds are, some part of sklearn covers all of that.

wooter 10y ago
having done ML R&D for a few years, they're docs are great for orienting newcomers to the field
geebee 10y ago
The scikit-learn tutorials are great. Another nice thing about scikit-learn is that the api for a lot of different ML algorithms is very similar, almost identical. This means that you can set up a train and and test set and swap in and out random forest, svm, naive bases, logistic regression, and various others. Read about them one by one, try to understand the algorithms generally, test them out, see how they perform differently on different data sets. It all depends on how you like to approach a new subject, but I think this is more fun and motivating than going straight into the mathematics behind the algorithms right away (which is more along the lines Andrew Ng's excellent course). I'd say once you're into it and using the algorithms, then dig deeper into the core mathematics, you'll have a better context for it.

sanderjd 10y ago

What is a good "hello world" project for machine learning? That is, what problem can I solve or question can I answer with minimal ceremony, and ideally with multiple techniques / technologies so that I can compare them? Is it this house price estimation like in your last link, or is there something better than that?

tsm 10y ago
The Iris data set [1] is very famous and a popular way to test out classification techniques. It's not "big data", but can be used to familiarize yourself with some basic data mining techniques. [1] – https://en.wikipedia.org/wiki/Iris_flower_data_set https://en.wikipedia.org/wiki/Iris_flower_data_set
sgk284 10y ago
Kaggle has a number of starter challenges. See https://www.kaggle.com/c/titanic https://www.kaggle.com/c/titanic for one related to predicting the survival of passengers on the Titanic.
- sanderjd 10y ago
  Nice, that is a great pointer.
- dschiptsov 10y ago
  Lol. Predicting the survival of passengers on the Titanic is meaningless and misleading - there is literally no connection to reality, despite the framing of the task which suggests a certain connection. There is absolutely nothing that could be predicted. It is just a simulation of oversimplified model which describes nothing, but an oversimplified view of a historical event. It is as meaningless as the ant simulator written by Rich Hickey to demonstrate features of Clojure - it has that much connection to real ants.
  - edgyswingset 10y ago
    Huh? Why would a connection to reality be required to get started with machine learning?

highd 10y ago

I would be pretty hesitant to start talking about TensorFlow and Deep Learning before confirming, for example, at least a rudimentary understanding of Linear Algebra.

akhilcacharya 10y ago

Regarding deep learning, what are some resources for learning strategies about improving network architectures? I read all of these architectures in research papers, but I'd really love to learn how to start iterating on them for a particular domain.