Slides from Keynotes at VII PythonBrasil

Hi all,

I'd like to share the slides of the keynotes I lectured at the VII PythonBrasil, the Brazilian Python Users Meeting that happens once a year.   This year I had the opportunity to give two talks: One is about the Open-Source Communities and the experience with the local community of Pernambuco: The Python User Group of Pernambuco (PUG-PE) and about the framework I am currently working on: Crab - A Python Framework for Building Recommender Systems.

It was an amazing event and with lots of amazing keynotes, opportunities to meet people and make some friends. I also had the opportunity to give two more lighting talks: the pipeline toolkit for scientific computations JobLib and about Ipython.

Below the slides provided:

Crab: A Python Framework for Building Recommender Systems

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The Crab slides for download.

JobLib: Running Python function as pipeline jobs

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                           The JobLib slides for download.

I'd like also to announce the launch of the new home page of the project Crab with a reformulated design. It still in development, with lots of work to do, but it's coming! The first release 0.1 will be launched until the second week of October.

Crab new Home Page 

Thanks for the feedback from all developers at PythonBrasil and I expect new contributors at the project.

Regards,

Marcel Caraciolo

Keynote about Scientific Computing with Python, Scipy, Numpy and Matplotib

Hi all,

I 'd like to share the slides from the keynote that I gave today at XV Pernambuco Python User Group Meeting about Scientific Computing with Python.  This keynote presents how you can start in the scientific world using several tools in Python such as Matplotlib, Scipy and Numpy.

Computação Científica com Python, Numpy e Scipy

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You can see it here.

It gives an overview about how you can start and basic introduction for those tools. It is in portuguese, since it was for a brazilian audience.

Any doubts, let me know.

Regards,

Marcel Caraciolo

Crab - Python Framework for Building Recommender Engines Video at Scipy 2011 Conference

Hi all,

My lecture at Scipy Conference 2011 is already available on-line in video, so you can watch me now presenting about our work at Muricoca Labs called "Crab - A Python Framework for Building Recommender Engines"  .  It is a work that I am developing with some machine learning developers as an alternative for python developers that want to work with recommender engines writing Python code.

Further information , it may be found here at the official home project. About my experience at Scipy, you can find at this post.

Here the link for the video,

Cheers,

Marcel Caraciolo

Scipy Conference 2011 and my participation!

Hi all,

Last week I was at the Scipy 2011 Conference at Austin, Tx. My first international conference as also my first lecture international! The Scipy Conference is an annual meeting for scientific computing developers and researchers that use python scientific packages in their research or work.  It was a great opportunity for meeting new python developers, know more about what's happening in scientific python nowadays and to learn about Scipy, Numpy and Matplotlib, considered the standard libraries for developers who wants start to develop in the scientific world.

At the first day of the conference, I had the opportunity to learn more about Numpy, a widely used library for numerical computations in Python as also learn more about the Scikit-learn framework, a great open-source toolkit for machine learning developers written in Python, Numpy and Scipy.  

You can access both tutorials available here at the Scipy Conference Tutorials WebPage.  Numpy is an amazing library, and what I learned I started already applied at the library I am currently working on called Crab for building recommender systems.   The Scikit-learn is also an interesting framework written in Scipy, Numpy and Matplotlib with several machine learning techniques and has as one main features the easy-to-use interface with lots of examples and tutorials for starters and beginners in machine learning.  It works so smoothly that I decided to use it as dependency of the Crab framework.

The second day started with more advanced tutorials, specially on Global Arrays with Numpy for  High performance computation. A quite powerful effort in this feature and I believe that soon will be added to the Numpy core. 

The another tutorial was about an introduction to Traits, Matplotlib and Chaco - great tools for creating nice user interfaces and plotting charts. One of the best parts of this tutorial was easily to create nice interfaces and animated plottings with a few lines of code.  Take a look of what you can do here or even see a real-time animated plotting with Matplotlib.

Traits and Chaco are part of the EPD package developed by the company Enthought, whose one of the co-founders is one of the main developers and founders of Numpy! Yeah :D Those frameworks allow easily create nice interfaces only using models concepts. If you want to learn more, please check out the tutorials as the official website about how to download, install and use it.

Another keynote interesting was about the Ipython, the incremented shell for scientific Python developers. What amazed me was when he showed the matplotlib embedded at the shell instead of opening a new window! The work around the Ipython has been fantastic, with several features for python developers! I extremely recommend!

The rest of the conference was dedicated to keynotes and talks about currently works on data science, core technologies and data mining with Python, Scipy , Numpy and related libraries.  I had the opportunity of giving the lecture - Crab - A Python Framework for Building Recommender Systems written by me, Bruno Melo and Ricardo Caspirro, actually the main contributors for this work.  The idea is to provide for python developers a recommender toolkit so they can easily create, test and deploy recommender engines with simple interfaces written with the scientific python packages such as Numpy, Scipy and Matplotlib.

Introduction to Crab - Python Framework for Building Recommender Systems

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You can check out my slides at the Scipy Conference here.

The project is currently being developed by the non-profitable organization called Muriçoca, that we decided  to create to manage and develop the Crab Framework. 

One of the best keynotes was the presentation of Hilary Manson, the Data Scientist at bit.ly.  She gave a funny lecture about her work and the current challenges with handling with large data sets and lots of URL-shortening happening at the backend of Bit.ly. It is quite amazing the amount of data and what you can do and extract useful information from all this data.

At least, I decided also to give a lighting talk about Mining the Scipy Lectures. A simple lecture to show what you can do with the data from the Scipy Conference Schedule and play with it. I used some NLP techniques and clustered based on the most frequent topics to check how was distributed the lectures at Scipy based on the keywords from their titles.  To visualize I used the Graph Visualization tool Ubigraph to show in 3D the clusters generated (by the way I used the K-means algorithm to cluster). 

Mining Scipy Lectures

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The slides are also available here and the source code here.

3D Lectures Clusters

Soon I will release the PDF with the article submitted as also the video with both keynotes that I presented.  It was an amazing conference at Austin, making new friends and lots of new partners! :D I expect to be there next year, absolutely!  One of my goals this year also is to prepare a scientific computing course using Python, wait for more information soon here at the blog (it will include matplotlib, scipy and numpy)!

Cheers,

Marcel Caraciolo

Crab: A Python Framework for Building Recommendation Engines

Hi all,

In this weekend I presented a lecture at the XII Python User Group Pernambuco Meeting about the framework I've been working on at these last months. The framework is called Crab and it is a Python library for building recommendation engines. Its main goal is to be an alternative for machine learning researchers and developers for use standard-of-the-art implemented recommendation algorithms, evaluate and extend it by building new techniques using the basic core provided by the framework.   

Me presenting the Crab : A Python framework for building recommendation engines

The framework has started in 2010, as a support toolkit for my master degree thesis about recommendation engines. I've implemented the Collaborative Filtering techniques as also the evaluation metrics used in recommendations (Precision, Recall, F1-Score, RMSE, etc). But since last month (April,2011) I decided to give the framework a shot to become more visible and bring more contributors for the project. The project became part of a Non-profitable organization called Muriçoca Labs, a team of  developers and researchers interested in Machine Learning and Artificial Intelligence. We also decided to    migrate all the core of the project using the scientific libraries Numpy, Scipy and Matplotlib, since the speed and the legibility of the code were the main advantages of these toolkits.   

The project started last month and had its first sprint where the team is focused on rewriting all the code of the old crab to this new release as also make it a independent project member of the Scikit repository - Sub-projects of Scipy Framework and a sub-module of the Scikit-Learn  (a popular python framework for machine learning algorithms). 

We are quite excited and working harder! By the way the Crab framework is already in production providing the recommendations of the brazilian social network AtePassar.  We are with lot of ideas and features such as content based filtering algorithms, recommendations as services providing REST APIs and Databases Models Support.

If you are interested in the project and want to know how to join us or use our projects, please let me know and add a comment below and I will be glad to help you!  The project is at the beginning, but we are working harder to see it in action as a possible alternative for Python developers that want to work with this hot topic in the data mining and web services: Recommender Systems.

Below I provide the slides that I presented about the project at the lecture.

Crab - A Python Framework for Building Recommendation Systems

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Meet the Muriçoca Labs here  and the link for the Crab framework here.

Regards,

Marcel Caraciolo