There is a nice example of machine learning with python and R in Analytics Vidhya and other tutorials, also ISLR introduction to statistical learning with R gives you an overview of some standard methods.
Ask HN: How to get started with machine learning?
91–100 of 131 posts
Re: Ask HN: How to get started with machine learning?
#92DON'T LEARN NEURAL NETWORKS FIRST. Instead, learn decision trees and more importantly enough statistics so you aren't dangerous. Do you know what the central limit theorem is and why it is important? Can you do 5-fold cross validation on a random forest model in your choice of tool? Fine, now you are ready to do deep learning stuff. The reason I say not to do neural networks first is because they aren't very effectiv…
I don't think it's a good strategy to discourage people from diving right in. There are many courses and books out there that are suitable even for a beginner who wants to learn about NN. Of course it's important to get a broad horizon eventually but starting with the theory without the applications is not how most humans learn best. Learning by doing is.
I agree strongly with the idea of diving in.
The problem with diving into neural networks is that they are slow to train (with large amounts of data anyway), and difficult to debug. This means it isn't really a great place to start.
Re: Ask HN: How to get started with machine learning?
#93If you want to jump right in with "hello world" type TensorFlow (a tool for machine learning), see 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 If you just want course materials from UC Berkeley, here's their 101 course: https://news.ycombinat…
I actually recommend jumping right into the excellent Scikit-learn tutorials, 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.
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.
Re: Ask HN: How to get started with machine learning?
#94If you want to jump right in with "hello world" type TensorFlow (a tool for machine learning), see 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 If you just want course materials from UC Berkeley, here's their 101 course: https://news.ycombinat…
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.
Re: Ask HN: How to get started with machine learning?
#95If you want to jump right in with "hello world" type TensorFlow (a tool for machine learning), see 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 If you just want course materials from UC Berkeley, here's their 101 course: https://news.ycombinat…
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.
Re: Ask HN: How to get started with machine learning?
#96Earlier quoted context omitted.
Kaggle has a number of starter challenges. See https://www.kaggle.com/c/titanic for one related to predicting the survival of passengers on the Titanic.
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 simulato…
It's very closely correlated to reality.
If you work through the data, you'll find things like women, children and first class passengers had a higher survival rate than men with lower class tickets[1].
This matches exactly the stories of what happened: Staff took first class passengers to the lifeboats first, then women and children. Then they ran out of lifeboats.
So the data shows correlation, and eye-witness accounts shows causation. That's close to the ideal combination: eyewitness accounts can be unreliable because we can't know how widespread they are, and correlation doesn't show causation.
But the combination of them both is pretty much the best case for studying something which can't be replicated.
[1] See examples like https://www.kaggle.com/omarelgabry/titanic/a-journey-through...
Re: Ask HN: How to get started with machine learning?
#97How is your programming background? Do some contest on hackerrank and gauge your skill because machine leaning uses lots of algorithm from math + computer science (eg computational geometry). Machine learning is basically writing some math in code and running experiment and statistically reason about result. If you really want to do that then you need to have a background in math + statistics + software development.
Or some challenge problems on SPOJ (http://www.spoj.com/problems/challenge/)
Re: Ask HN: How to get started with machine learning?
#98Earlier quoted context omitted.
Correlations does not imply causation. There were many more relevant but "invisible" variables, which, probably, related to some genetic factors, like ability to sustain exposure to the cold water, ability to calm oneself down to avoid panic and self-control in general, strong survival instinct to literally fight the others, etc. The variables you have described, except the age of a passenger, are visible but irrelev…
This challenge is not about causal inference. I do agree it is more of a toy dataset, to get started with the basics, and that there are a lot of other variables that go into survivability. But to say these variables, except for age, are irrelevant is mathematically unsound: You can show with cross-validation and test set performance that your model using these variables generalizes (around 0.80 ROC AUC). You can do…
> You can show with cross-validation and test set performance that your model using these variables generalizes (around 0.80 ROC AUC).
It shows only that given set of variables (observable and inferred) could be used to build a model. The given data set is not descriptive, because it does not contain more relevant hidden variables, so any predictions or inferences based on this data set are nothing but a story, a myth made from statistics and data.
Re: Ask HN: How to get started with machine learning?
#99Earlier quoted context omitted.
There is a very important notion from The Sciences of the Artificial book by Herbert A. Simon, that the visible (to an external observer) behavior of an ant (its tracks, if you wish) is not due to its supposed intelligence, but mostly due to the obstacles in the environment. Most of the models mimic and simulate (very naively) that observable behavior, not its origin. When people cite "the map is not the territory" t…
It is swarm intelligence: How does the system keep finding successful paths in a changing environment? Can we take inspiration from this behavior to create better optimization algorithms? Simulation can be a very beneficial experiment. See for instance: https://papers.nips.cc/paper/5351-searching-for-higgs-boson-...
Why not. I remember a paper which compares the behavior of foraging ants (they send more or less ants according to the rate of returned with food) to adjustment of the window size based on the data rate in TCP.
Simulations are not experiments. It is an animation of a formalized imagination, if you wish.
Re: Ask HN: How to get started with machine learning?
#100Earlier quoted context omitted.
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 simulato…
It's very closely correlated to reality. If you work through the data, you'll find things like women, children and first class passengers had a higher survival rate than men with lower class tickets[1]. This matches exactly the stories of what happened: Staff took first class passengers to the lifeboats first, then women and children. Then they ran out of lifeboats. So the data shows correlation, and eye-witness acco…
This is only one of many aspects of that event. The data reflects that the efforts of organized evacuation in the beginning were efficient.
But any attempt to frame it as a "prediction", an accurate model of the event or adequate description of reality is just nonsense.
To call things by its proper names (precise use of the language) is the foundation of the scientific method. This is mere oversimplified, non-descriptive toy model of one aspect of historical event, made from of statistics of partially observable environment. A few inferred correlations reflects that there was not a total chaos, but some systematic activity. No doubt about it. But this is absolutely unscientific to say anything else about the toy model, let alone claim that any predictions based on it have any connection to reality.