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Show HN: Python Machine Learning – A Crash Course

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Re: Show HN: Python Machine Learning – A Crash Course

#11

Post by faizshah »

deskamess wrote: post #10
EForEndeavour wrote: post #9

Earlier quoted context omitted.

The code itself is indeed simple, thanks to the combined efforts of very smart and capable researchers and developers across the world. But the time taken to write the actual code to perform ML is negligible compared to: - choosing the right algorithm(s) for the specific task and data at hand - tuning hyperparameters - interpreting preliminary results and/or the output of statistical tests - chasing down and cleaning…

Do you have any references that could help with 'choosing the right algorithm'? That seems to be something that comes with experience, or knowing how a particular algorithm works, or more importantly, does not work.

This one has been recommended to me a lot: https://scikit-learn.org/stable/tutorial/machine_learning_ma...

But I agree I wish there was more resources on that, it seems to be just a trial and error process.

Re: Show HN: Python Machine Learning – A Crash Course

#12

Post by gchamonlive »

deskamess wrote: post #10
EForEndeavour wrote: post #9

Earlier quoted context omitted.

The code itself is indeed simple, thanks to the combined efforts of very smart and capable researchers and developers across the world. But the time taken to write the actual code to perform ML is negligible compared to: - choosing the right algorithm(s) for the specific task and data at hand - tuning hyperparameters - interpreting preliminary results and/or the output of statistical tests - chasing down and cleaning…

Do you have any references that could help with 'choosing the right algorithm'? That seems to be something that comes with experience, or knowing how a particular algorithm works, or more importantly, does not work.

For my final project in control engineering I was tasked with writing a couple of ml algorithms and rank them to suggest the best one. The code I was working on was not good at all and all the data was being processed in Matlab, so I spent all the project refactoring and proposing a viable solution to using python as a math backend for the java application.

Anyways I had to research a little into the subject and what I found is that there isn't a straightforward approach to choosing algorithms. I could be mistaken, but I believe the best approach for you would be to get intimate knowledge from every ml algorithm and maybe use a cheatsheet to guide you, but ultimately only knowing well your data set (distribution of classes, occurrences, which traits are better for which categorization you want to do etc..) will bring you farther than looking for a recipe for choosing an algorithm.

Re: Show HN: Python Machine Learning – A Crash Course

#13

Post by jononor »

deskamess wrote: post #10
EForEndeavour wrote: post #9

Earlier quoted context omitted.

The code itself is indeed simple, thanks to the combined efforts of very smart and capable researchers and developers across the world. But the time taken to write the actual code to perform ML is negligible compared to: - choosing the right algorithm(s) for the specific task and data at hand - tuning hyperparameters - interpreting preliminary results and/or the output of statistical tests - chasing down and cleaning…

Do you have any references that could help with 'choosing the right algorithm'? That seems to be something that comes with experience, or knowing how a particular algorithm works, or more importantly, does not work.

One possible strategy is:

1. Find out what kind of information that is wanted from the model and what kind of data you have available that could help with this. 1b) And note down other requirements for the solution.

2. Find out how to formulated the information needs in terms of a well-understood and well-researched problem.

3. Find out what the current best performant and well-understood solutions are for this problem. Usually through a literature search.

4. Choose a few candidates, and rank them wrt your particular requirements.

5. Test out 1-3 of them

This is a business-needs-first, top-down type method. Prior knowledge of the details of a lot of algorithms not needed. Ability to understand terminology and quickly identify relevant material/papers critical. A good overview of common problem formulations and methods will build over time, and speeds things up immensely.

Re: Show HN: Python Machine Learning – A Crash Course

#14

Post by rahimnathwani »

The tutorials introduce how the different algorithms work, but the code just uses the libraries rather than implementing what's in the library from primitive operations.

For me, this type of tutorial doesn't stick. I found the explanations in Joel Grus' book, which were accompanied by succinct, idiomatic Python implementations of the algorithms, much easier to understand.

Re: Show HN: Python Machine Learning – A Crash Course

#15

Post by Buetol »

Speaking of Machine learning, I love the docker images of tensorflow. Got Tensorflow running with an IPython UI in less than 2 min with only one command just 5 minutes ago:

>> docker run -it --rm -v $(realpath ~/notebooks):/tf/notebooks -p 8888:8888 tensorflow/tensorflow:latest-py3-jupyter

Re: Show HN: Python Machine Learning – A Crash Course

#16

Post by WrtCdEvrydy »

Buetol wrote: post #15

Speaking of Machine learning, I love the docker images of tensorflow. Got Tensorflow running with an IPython UI in less than 2 min with only one command just 5 minutes ago: >> docker run -it --rm -v $(realpath ~/notebooks):/tf/notebooks -p 8888:8888 tensorflow/tensorflow:latest-py3-jupyter

You can actually add nightly-gpu-py3-jupyter if you'd like GPU level tensorflow as well :D

Re: Show HN: Python Machine Learning – A Crash Course

#17

Post by rytill »

girlsrule1234 wrote: post #3
WilliamEdward wrote: post #2

I swear an AI crash course gets posted to HN every other day..

It certainly does seem that way, especially with python. I wouldn’t be opposed to a fun tutorial in a different language. Also not trying to be pedantic (is anyway) but ... should always have three .’s. (I learned that recently)

Actually, if it's at the end of a sentence, it should have four '.'s. Three for the ellipsis, and one for the end of the sentence....

Re: Show HN: Python Machine Learning – A Crash Course

#18

Post by ackbar03 »

WilliamEdward wrote: post #2

I swear an AI crash course gets posted to HN every other day..

I have to agree actually. If one is actually serious in wanting to learn this stuff he really should just do a Google search, there doesnt really need to be anymore of these

Re: Show HN: Python Machine Learning – A Crash Course

#19

Post by krick »

I wonder if there is some course that would cover a bit more advanced topics in a comprehensible manner. There are hundreds of courses/books/tutorials that cover pretty much the same stuff again and again.

General ML: supervised vs unsupervised, K-means clustering, linear regression, logistic regression, maybe several enseble learning methods based on trees.

NNs: backpropagation, gradient descent, tensorflow, a bit about meta-param selection, CNNs (basically, just ImageNet), sometimes RNNs are mentioned.

This is all pretty entry-level and covered many times over, but, surprisingly, that's pretty much it. Discussion of models pretty much stops at ImageNet. I rarely see RBM or autoencoder, and pretty much nothing about how real problems are encoded into inputs and outputs.

I am ashamed to admit, but I still don't really understand how AlphaZero, AlphaStar or various language models (GPT, BERT) really work. Is there something good on that, maybe?

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