rasbt

36 ms·

1. 151.▲
   Why Implement Machine Learning Algorithms from Scratch?(kdnuggets.com)

   4 points by rasbt 10y ago|0 comments
2. 152.▲
   The Development of Classification as a Learning Machine(kdnuggets.com)

   2 points by rasbt 10y ago|0 comments
3. 153.▲
   Answers to Frequently Asked Questions in Machine Learning(sebastianraschka.com)

   4 points by rasbt 11y ago|0 comments
4. 154.▲
   Machine Learning FAQ – you asked me everything(sebastianraschka.com)

   6 points by rasbt 11y ago|0 comments
5. 155.▲
   Doing Data Science at Twitter – A reflection of my two year Journey so far(medium.com)

   2 points by rasbt 11y ago|0 comments
6. 156.▲
   by rasbt 11y ago

   Yes, I think you are right. Out of curiosity, when I browsed over Coursera's course catalog, most data science related material seems to be taught in Matlab or R (however, there are also others, e.g., Klein's Linear Algebra class
7. 157.▲
   by rasbt 11y ago

   I agree with you. However, note that many people who are using Python for writing scientific code make use of C/C++ in one way or the other (aside from NumPy, SciPy, and Theano). For example, many people write the "most intensive&
8. 158.▲
   by rasbt 11y ago

   Thanks for the insights! Also here, this comment would make an interesting addition to a "Feedback" section at the end of the article to give people a broader view on this topic. May I have your permission to post your comment bel
9. 159.▲
   by rasbt 11y ago

   Just want to leave a comment about the "Matrix" object. I remember the first day I got started with NumPy. Coming from MATLAB, I really felt tempted to use it. However, I also saw an recommendation (can't remember if I read t
10. 160.▲
    by rasbt 11y ago

    Nice collection, let me add one more item to this list: Seaborn: statistical data visualization: http://stanford.edu/~mwaskom/software/seaborn/
11. 161.▲
    by rasbt 11y ago

    Interesting! It's hard for a single person to tell, because you can only start one way digging into machine learning. However, from a teacher's perspective, this is a useful observation. I also started with Matlab since it was the
12. 162.▲
    by rasbt 11y ago

    I went through the very same process :). I really like your comment, you highlight something that I forgot to mention in this clarity "It makes it easier for me to collaborate in many different ways: share tools with other teams, trans
13. 163.▲
    by rasbt 11y ago

    Thanks for the comment (I am the author of this article). > I have a feeling the author will find their way to Julia-land eventually, in a couple of years or so. I have a strong feeling that this will eventually happen :). In an ideal,
14. 164.▲
    by rasbt 11y ago

    I wholeheartedly recommend [scikit-learn]( https://github.com/scikit-learn/scikit-learn ) - the best organized and cleanest code I've seen so far. It is really organized and well thought-through.
15. 165.▲
    The ELM Scandal, a Formal Complaint Launched Against Extreme Learning Machines(theanonymousemail.com)

    3 points by rasbt 11y ago|0 comments
16. 166.▲
    A glimpse of the early history of machine learning – artificial neurons(sebastianraschka.com)

    6 points by rasbt 12y ago|0 comments
17. 167.▲
    Map Your Google Location History in Python(chrisalbon.com)

    3 points by rasbt 12y ago|0 comments
18. 168.▲
    Intro to Supervised Machine Learning and Classification: The Big Picture(slides)(slideshare.net)

    2 points by rasbt 12y ago|0 comments
19. 169.▲
    Things in Python Pandas I Wish I'd Had Known Earlier(nbviewer.ipython.org)

    1 points by rasbt 12y ago|0 comments
20. 170.▲
    Principal Component Analysis in Simple Steps(sebastianraschka.com)

    8 points by rasbt 12y ago|0 comments
21. 171.▲
    Implementing a Weighted Majority Rule Ensemble Classifier in Scikit-learn(sebastianraschka.com)

    2 points by rasbt 12y ago|0 comments
22. 172.▲
    Star Wars API: The First Data Viz(nbviewer.ipython.org)

    4 points by rasbt 12y ago|0 comments
23. 173.▲
    MusicMood. A Machine Learning Approach to Classify Music by Mood Based on Lyrics(sebastianraschka.com)

    3 points by rasbt 12y ago|0 comments
24. 174.▲
    Turn Your Twitter Timeline into a Word Cloud Using Python(sebastianraschka.com)

    4 points by rasbt 12y ago|0 comments
25. 175.▲
    Why so sad? The mood of music over the last 50 years [pdf](sebastianraschka.com)

    5 points by rasbt 12y ago|0 comments
26. 176.▲
    Hierarchical Agglomerative Clustering and Heatmaps in Python(nbviewer.ipython.org)

    4 points by rasbt 12y ago|0 comments
27. 177.▲
    Data-collection command line tools (lyrics, twitter timeline, music tags)(github.com)

    2 points by rasbt 12y ago|0 comments
28. 178.▲
    by rasbt 12y ago

    I posted this article some while ago, and I thought it may not be a bad idea to archive it on arXiv. What do you think? The (Latex) PDF version would look like this: http://sebastianraschka.com/PDFs/articles/naive_
29. 179.▲
    by rasbt 12y ago

    In practice, I would say that it is a very challenging task to determine whether variables are totally independent or not: It is more about the strength of independence. Every time something is "estimated", you'd introduce an
30. 180.▲
    by rasbt 12y ago

    Thanks for the comment, and I agree! I rephrased it a little bit to "Empirical comparisons provide evidence that the multinomial model tends to outperform the multi-variate Bernoulli model if the vocabulary size is relatively large [1