List of videos

"pybind11 - seamless operability between C++11 and Python [EuroPython 2017 - Talk - 2017-07-14 - PythonAnywhere Room] [Rimini, Italy] https://github.com/pybind/pybind11 pybind11 is a lightweight header-only C++11 library that exposes C++ types to Python and vice versa and allows creating Python extension modules with minimum boilerplate by leveraging compile-time introspection and type inference. While this library's goals and some of the syntax may be considered similar to Boost.Python, it has a much smaller footprint, is entirely self-contained, and offers additional features like direct support for NumPy arrays. In this talk, we will look at how to write Python extension modules in C++ from scratch with pybind11, starting from simple bindings and building up to more complex examples that deal with iterators, STL data structures, NumPy types and Python callbacks. We will also touch upon some of the internal machinery of the library like the virtual call mechanism and reference counting. License: This video is licensed under the CC BY-NC-SA 3.0 license: https://creativecommons.org/licenses/by-nc-sa/3.0/ Please see our speaker release agreement for details: https://ep2017.europython.eu/en/speaker-release-agreement/

"Overcoming Cognitive Bias [EuroPython 2017 - Talk - 2017-07-14 - Anfiteatro 2] [Rimini, Italy] Starting with a brief description of how built-in mechanisms in our brains lead to cognitive bias, the talk will address how a variety of cognitive biases manifest in the Python and tech communities, and how to overcome them. License: This video is licensed under the CC BY-NC-SA 3.0 license: https://creativecommons.org/licenses/by-nc-sa/3.0/ Please see our speaker release agreement for details: https://ep2017.europython.eu/en/speaker-release-agreement/

"From an old-school data managing company to data analytics with Python [EuroPython 2017 - Talk - 2017-07-14 - Anfiteatro 2] [Rimini, Italy] Our mission is to manage a huge amount of communication and document data in large scale industry projects by providing web based project management systems. The increasing amount of communication creates the desire for a GPS helping us and our customers to navigate through the communication stream. Our R&D projects are focusing on topics like clustering, event detection, and network analysis (Who knows who, domain experts). Traveling the wild side of NLP, Data Science, and Analytics, we stumbled across amazing Python tools supporting us in our goal to navigate the project communication and therefor supporting our clients in Project & Risk Management avoiding wrong turns. We would like to share some of our approaches to answer our research topics and challenges: One of the challenges, amongst others, is to utilize and adapt up to date clustering algorithms for social stream data and to expose them as reentrant services. Another one is to tailor them for the current application domain, improving clustering precision by parametrization and other means. Furthermore the integration of a Python based analytics system into an existing JAVA based application environment and eco system is required. In addition, we would also like to share some of our ""traffic jams"" experienced during our travel starting as traditional Java/SQL focusing company that integrated Python into its development portfolio. License: This video is licensed under the CC BY-NC-SA 3.0 license: https://creativecommons.org/licenses/by-nc-sa/3.0/ Please see our speaker release agreement for details: https://ep2017.europython.eu/en/speaker-release-agreement/

"PyPy meets Python 3 and Numpy [EuroPython 2017 - Talk - 2017-07-14 - Anfiteatro 2] [Rimini, Italy] PyPy is an alternative Python implementation whose JIT often gives seriously better performance than CPython. Now PyPy supports, in beta version, two major new application domains: Python 3.x, and Numpy and the rest of the scientific stack. These are each an important milestone for a subset of the Python community. Thanks to a grant by Mozilla, ""PyPy3"" now largely supports Python 3.5 with one or two extensions from Python 3.6. Full support should be very close. (Note that PyPy2 will not disappear, if only because PyPy itself is written in Python 2.7.) Numpy and the major packages of the scientific stack are now starting to work well with PyPy (PyPy2 mostly, but also PyPy3). This is thanks to progress in ""cpyext"" emulating the CPython C API, as well as fixes to the packages in collaboration with the upstream developers. We will also mention some more ""what's new in PyPy"" topics from the last couple of years. License: This video is licensed under the CC BY-NC-SA 3.0 license: https://creativecommons.org/licenses/by-nc-sa/3.0/ Please see our speaker release agreement for details: https://ep2017.europython.eu/en/speaker-release-agreement/

"Scientific computing using Cython: Best of both Worlds! [EuroPython 2017 - Talk - 2017-07-14 - Anfiteatro 2] [Rimini, Italy] Cython is not only an excellent and widely used tool to speed up computational Python code, it’s also a very smart way to talk to native code and libraries. The Cython compiler translates Python code to C or C++ code, and supports static type annotations to allow direct use of C/C++ data types and functions. You get the best of both worlds while working with Cython: Python like syntax with blazing fast C speed. This talk/tutorial by a Python/Cython developer introduces Cython programming language and leads the participants all the way from their first Python extension to an efficient integration with native C. Topics covered will be: 1. Using the Cython compiler to build a native extension module 2. Cython development from Jupyter notebook 3. Mixing Python with static C types in the Cython language 4. Calling into native code from Cython code (Brief introduction) 5. Wrap up: A brief case study Cyvlfeat: A Cython/Python wrapper for Computer Vision library, VLFeat. Participants are expected to have a good understanding of the Python language, some basic knowledge about C or C++. No deep C programming knowledge is required, nor is any prior knowledge needed about writing extension modules for the CPython runtime. License: This video is licensed under the CC BY-NC-SA 3.0 license: https://creativecommons.org/licenses/by-nc-sa/3.0/ Please see our speaker release agreement for details: https://ep2017.europython.eu/en/speaker-release-agreement/

"The Encounter: Python’s adventures in Africa [EuroPython 2017 - Keynote - 2017-07-14 - Anfiteatro 2] [Rimini, Italy] A genuine encounter changes both parties. In this talk Daniele and Aisha will report on the dialogue opened up by recent PyCons and other Python events in Africa. They’ll discuss Python’s impact in countries including Namibia, Nigeria and Zimbabwe, and what open-source software means for Africa at large - and what the encounter means for Python too. License: This video is licensed under the CC BY-NC-SA 3.0 license: https://creativecommons.org/licenses/by-nc-sa/3.0/ Please see our speaker release agreement for details: https://ep2017.europython.eu/en/speaker-release-agreement/

"Modelling pollution from traffic, using Smartphone data and Python [EuroPython 2017 - Talk - 2017-07-14 - Anfiteatro 1] [Rimini, Italy] The talk presents results from my PhD project on models for transportation related pollution. Pollution from personal transport in Cities is a big and growing problem. By monitoring the flow, and congestion in the transport system two goals can be achieved. First, the adherence to agreed limit values (or breaking said limits) can be followed and used to decrease health effects of local pollution hotspots. Secondly, monitoring of the total emission of climate forcing gases from transportation, is important for designing climate mitigation actions. Python is used in combination with other tools to convert sensor data from smartphones, into pollution concentrations in urban settings. To mitigate the lack of complete data coverage, the missing data is simulated by a traffic model, to locate congestion and model the traffic related pollution concentration. License: This video is licensed under the CC BY-NC-SA 3.0 license: https://creativecommons.org/licenses/by-nc-sa/3.0/ Please see our speaker release agreement for details: https://ep2017.europython.eu/en/speaker-release-agreement/

"Large-scale data extraction, structuring and matching using Python and Spark [EuroPython 2017 - Talk - 2017-07-14 - Anfiteatro 1] [Rimini, Italy] Motivation - Matching data collections with the aim to augment and integrate the information for any available data point that lies in two or more of these collections, is a problem that nowadays arises often. Notable examples of such data points are scientific publications for which metadata and data are kept in various repositories, and users’ profiles, whose metadata and data exist in several social networks or platforms. In our case, collections were as follows: (1) A large dump of compressed data files on s3 containing archives in the form of zips, tars, bzips and gzips, which were expected to contain published papers in the form of xmls and pdfs, amongst other files, and (2) A large store of xmls in the form of xmls, some of which are to be matched to Collection 1. Problem Statement - The problems, then, are: (1) How to best unzip the compressed archives and extract the relevant files? (2) How to extract meta-information from the xml or pdf files? (3) How to match the meta-information from the two different collections? And all of these must be done in a big-data environment. Presentation – https://drive.google.com/open?id=1hA9J80446Qh7nd8PMYZibtIR1WjMkdLXfDgwUlts7JM The presentation will describe the solution process and the use of python and Spark in the large-scale unzipping and extraction of files from archives, and how metadata was then extracted from the files to perform the matches on. License: This video is licensed under the CC BY-NC-SA 3.0 license: https://creativecommons.org/licenses/by-nc-sa/3.0/ Please see our speaker release agreement for details: https://ep2017.europython.eu/en/speaker-release-agreement/

"Introduction to TensorFlow [EuroPython 2017 - Talk - 2017-07-14 - Anfiteatro 1] [Rimini, Italy] Deep learning is at its peak, with scholars and startups releasing new amazing applications every other week, and TensorFlow is the main tool to work with it. However, Tensorflow it's not an easy-access library for beginners in the field. In this talk, we will cover the explanation of core concepts of deep learning and TensorFlow totally from scratch, using simple examples and friendly visualizations. The talk will go through the next topics: • Why deep learning and what is it? • The main tool for deep learning: TensorFlow • Installation of TensorFlow • Core concepts of TensorFlow: Graph and Session • Hello world! • Step by step example: learning how to sum • Core concepts of Deep Learning: Neural network • Core concepts of Deep Learning: Loss function and Gradient descent By the end of this talk, the hope is that you will have gained the basic concepts involving deep learning and that you could build and run your own neural networks using TensorFlow. License: This video is licensed under the CC BY-NC-SA 3.0 license: https://creativecommons.org/licenses/by-nc-sa/3.0/ Please see our speaker release agreement for details: https://ep2017.europython.eu/en/speaker-release-agreement/