> My book will actually have much less > math (no linear algebra, no derivatives, no probabilities).
I think that’s a very good thing :). Based on your talks, which brings us back to SciPy ;), I think it will be just great! I expect your book to be the definitive guide to scikit-learn, something that the many programmers and computer scientists out there want and need (and they can still pick up Bishop’s book later on if they want to dive into the mathy stuff :P). Since I am not an scikit-learn core dev (and by no means an expert), I thought it was somewhat inappropriate for me to write a book entirely about scikit-learn. Thus, I approached the topic from a more conceptual perspective and used scikit-learn to provide the practical examples :P. If it were up to me, I would have chosen a much slower pace and implemented more from scratch to provide further insights into the algorithms and leave the scikit-learn sections for a separate title such as the one you are writing, but yeah, the page limits … (plus, I only had 3 months for writing — in the after-work hours — or in other words: 1 week per chapter; definitely something I will never ever commit to again in my life :D ). > I would base the material on last year probably, unless you have better > material ;) > I have several improvements, I think, and I'd polish it a bit. Yeah, why re-inventing the wheel :) I watched (part of) the tutorial Kyle and you gave last year, and I think it’s really good overall! I was just wondering if it would be possible to present/introduce the topics a bit differently. E.g., using presentation slides with more figures to introduce each topic first, and then using the IPython notebook for the interactive exercises. My idea would be to focus on “what are we doing?” and "why are we doing it?” first before we walk through the code & tools to accomplish this task. So, what I am suggesting is that I could create a short 5 min intro presentation for each notebook where I talk about the algorithms (e.g., explaining what a cost function is, what logistic regression is and how it works etc.; that KNN is a lazy learner, when to use it, explain its advantages and disadvantages and so forth). As an additional topic, if there’s time, I’d suggest adding a case-study for out-of-core learning via the SGD classifiers, I think that may be really useful since a lot of people may have to work with datasets that don’t fit into memory at some point (or do you already have a notebook on that and I just overlooked it?). What do you think? Sorry, but I see that the proposal deadline is approaching rather quickly ... I am really new to this and was wondering how we best proceed (in case you want to have me onboard :P)? How can I help with the proposal, and would you mind sending me the proposal from last year to get a rough idea of how a successful proposal looks like? > On Mar 13, 2016, at 5:02 PM, Andreas Mueller <t3k...@gmail.com> wrote: > > Just bought the book on amazon ;) > It's interesting. Have you read Marslands book by any chance? > It has a somewhat similar approach. My book will actually have much less > math (no linear algebra, no derivatives, no probabilities). > Let's see how that will go down lol. > > I would base the material on last year probably, unless you have better > material ;) > I have several improvements, I think, and I'd polish it a bit. > > > > On 03/11/2016 02:18 AM, Sebastian Raschka wrote: >> Sure, I’d be all in! That’s actually perfect timing; I was just about to >> prepare my proposal for a CompBio talk at SciPy (talking about my >> large-scale virtual screening framework, where I also use ML for predictive >> modeling of chemical groups in target molecules), but yeah, I’d rather like >> to talk about scikit-learn since I am going to an CompBio conference in May >> already anyway. >> >>> I'm about to read your book the next couple of days ;) >> Haha, that sounds like a pretty boring task :); if you don’t have a copy, >> yet, please let me know, I’d be happy to send one along (print & ebook). >> >>> Can you maybe have a look at last year's material? >> Will do. The deadline is not that far away (March 21, right?). Do you >> already have in mind what you’d like to talk about in particular? >> >> Best, >> Sebastian >> >>> On Mar 10, 2016, at 7:59 PM, Andreas Mueller <t3k...@gmail.com> wrote: >>> >>> Sebastian: looks like it will be on us ;) >>> Can you maybe have a look at last year's material? >>> I'm about to read your book the next couple of days ;) >>> >>> Sent from phone. Please excuse spelling and brevity. >>> >>> On Feb 22, 2016 12:20, "Sebastian Raschka" <se.rasc...@gmail.com> wrote: >>> After missing all the fun last year, I am also planning on attending — I’d >>> also be happy to help if there’s a shortage in core devs for the tutorials >>> ;) >>> >>> Cheers, >>> Sebastian >>> >>>> On Feb 22, 2016, at 12:11 PM, Manoj Kumar <manojkumarsivaraj...@gmail.com> >>>> wrote: >>>> >>>> Hi everyone. >>>> >>>> I'll definitely be happy to help on the tutorial! >>>> >>>> On Mon, Feb 22, 2016 at 11:41 AM, Andreas Mueller <t3k...@gmail.com> wrote: >>>> Who's going? >>>> I'll definitely be there and am happy to do a tutorial. >>>> Who's in? >>>> >>>> >>>> >>>> On 02/22/2016 04:15 AM, Nelle Varoquaux wrote: >>>>> Dear all, >>>>> >>>>> SciPy 2016, the Fifteenth Annual Conference on Python in Science, takes >>>>> place in Austin, TX on July, 11th to 17th. The conference features two >>>>> days of tutorials by followed by three days of presentations, and >>>>> concludes with two days of developer sprints on projects of interest to >>>>> attendees. . >>>>> >>>>> The topics presented at SciPy are very diverse, with a focus on advanced >>>>> software engineering and original uses of Python and its scientific >>>>> libraries, either in theoretical or experimental research, from both >>>>> academia and the industry. This year we are happy to announce two >>>>> specialized tracks that run in parallel to the general conference (Data >>>>> Science , High Performance Computing) and 8 mini-symposia (Earth and >>>>> Space Science, Biology and Medicine, Engineering, Social Sciences, >>>>> Special Purpose Databases, Case Studies in Industry, Education, >>>>> Reproducibility) >>>>> >>>>> Submissions for talks and posters are welcome on our website >>>>> (http://scipy2016.scipy.org). In your abstract, please provide details on >>>>> what Python tools are being employed, and how. The talk and poster >>>>> submission deadline is March 25th, 2016, while the tutorial submission >>>>> deadline is March, 21st, 2016. >>>>> >>>>> >>>>> Important dates: >>>>> >>>>> Mar 21: Tutorial Proposals Due >>>>> Mar 25: Talk and Poster Proposals Due >>>>> May 11: Plotting Contest Submissions Due >>>>> Apr 22: Tutorials Announced >>>>> Apr 22: Financial Aid Submissions Due >>>>> May 4: Talk and Posters Announced >>>>> May 11: Financial Aid Recipients Notified >>>>> May 22: Early Bird Registration Deadline >>>>> Jul 11-12: SciPy 2016 Tutorials >>>>> Jul 13-15: SciPy 2016 General Conference >>>>> Jul 16-17: SciPy 2016 Sprints >>>>> >>>>> We look forward to an exciting conference and hope to see you in Austin >>>>> in July! >>>>> >>>>> >>>>> The Scipy 2016 >>>>> http://scipy2016.scipy.org/ >>>>> >>>>> Conference Chairs: Aric Hagberg, Prabhu Ramachandran >>>>> Tutorial Chairs: Justin Vincent, Ben Root >>>>> Program Chair: Serge Rey, Nelle Varoquaux >>>>> Proceeding Chairs: Sebastian Benthall >>>>> >>>>> >>>>> >>>>> ------------------------------------------------------------------------------ >>>>> Site24x7 APM Insight: Get Deep Visibility into Application Performance >>>>> APM + Mobile APM + RUM: Monitor 3 App instances at just $35/Month >>>>> Monitor end-to-end web transactions and take corrective actions now >>>>> Troubleshoot faster and improve end-user experience. Signup Now! >>>>> >>>>> http://pubads.g.doubleclick.net/gampad/clk?id=272487151&iu=/4140 >>>>> >>>>> >>>>> _______________________________________________ >>>>> Matplotlib-users mailing list >>>>> >>>>> matplotlib-us...@lists.sourceforge.net >>>>> https://lists.sourceforge.net/lists/listinfo/matplotlib-users >>>> >>>> ------------------------------------------------------------------------------ >>>> Site24x7 APM Insight: Get Deep Visibility into Application Performance >>>> APM + Mobile APM + RUM: Monitor 3 App instances at just $35/Month >>>> Monitor end-to-end web transactions and take corrective actions now >>>> Troubleshoot faster and improve end-user experience. Signup Now! >>>> http://pubads.g.doubleclick.net/gampad/clk?id=272487151&iu=/4140 >>>> _______________________________________________ >>>> Scikit-learn-general mailing list >>>> Scikit-learn-general@lists.sourceforge.net >>>> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general >>>> >>>> >>>> >>>> >>>> -- >>>> Manoj, >>>> http://github.com/MechCoder >>>> ------------------------------------------------------------------------------ >>>> Site24x7 APM Insight: Get Deep Visibility into Application Performance >>>> APM + Mobile APM + RUM: Monitor 3 App instances at just $35/Month >>>> Monitor end-to-end web transactions and take corrective actions now >>>> Troubleshoot faster and improve end-user experience. Signup Now! >>>> http://pubads.g.doubleclick.net/gampad/clk?id=272487151&iu=/4140_______________________________________________ >>>> Scikit-learn-general mailing list >>>> Scikit-learn-general@lists.sourceforge.net >>>> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general >>> >>> ------------------------------------------------------------------------------ >>> Site24x7 APM Insight: Get Deep Visibility into Application Performance >>> APM + Mobile APM + RUM: Monitor 3 App instances at just $35/Month >>> Monitor end-to-end web transactions and take corrective actions now >>> Troubleshoot faster and improve end-user experience. Signup Now! >>> http://pubads.g.doubleclick.net/gampad/clk?id=272487151&iu=/4140 >>> _______________________________________________ >>> Scikit-learn-general mailing list >>> Scikit-learn-general@lists.sourceforge.net >>> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general >>> ------------------------------------------------------------------------------ >>> Transform Data into Opportunity. >>> Accelerate data analysis in your applications with >>> Intel Data Analytics Acceleration Library. >>> Click to learn more. >>> http://pubads.g.doubleclick.net/gampad/clk?id=278785111&iu=/4140_______________________________________________ >>> Scikit-learn-general mailing list >>> Scikit-learn-general@lists.sourceforge.net >>> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general >> >> ------------------------------------------------------------------------------ >> Transform Data into Opportunity. >> Accelerate data analysis in your applications with >> Intel Data Analytics Acceleration Library. >> Click to learn more. >> http://pubads.g.doubleclick.net/gampad/clk?id=278785111&iu=/4140 >> _______________________________________________ >> Scikit-learn-general mailing list >> Scikit-learn-general@lists.sourceforge.net >> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general > > > ------------------------------------------------------------------------------ > Transform Data into Opportunity. > Accelerate data analysis in your applications with > Intel Data Analytics Acceleration Library. > Click to learn more. > http://pubads.g.doubleclick.net/gampad/clk?id=278785111&iu=/4140 > _______________________________________________ > Scikit-learn-general mailing list > Scikit-learn-general@lists.sourceforge.net > https://lists.sourceforge.net/lists/listinfo/scikit-learn-general ------------------------------------------------------------------------------ Transform Data into Opportunity. 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