Yves Hilpisch (@dyjh)<\/p>\n
Not too long ago, the finance field was dominated by compiled languages, such as C or C++, since they were considered to be the right choice for the implementation of computationally demanding algorithms.<\/p>\n
Over the last couple of years, Python and its fast growing eco-system of (data analysis) packages have made it today's language of choice for the financial industry.<\/p>\n
This talk illustrates, based on some simple examples, the benefits of working with a high level programming language, such as Python, for the tasks at hand in finance. The examples illustrate that really concise Python code can achieve high performance results.<\/p>\n
Recorded at<\/strong> PyCon.DE 2017 Karlsruhe: https:\/\/de.pycon.org\/<\/a><\/p>\n Video editing<\/strong>: Sebastian Neubauer & Andrei Dan<\/p>\n Tools<\/strong>: Blender, Avidemux & Sonic Pi<\/p>\n","pubDate":"Wed, 25 Oct 2017 00:00:00 +0000","guid":"tag:pyvideo.org,2017-10-25:\/pycon-de-2017\/why-python-has-taken-over-finance.html","category":["PyCon DE 2017","algorithms","performance","finance","python"]},{"title":"Deep learning: the final frontier for time series analysis and signal processing?","link":"https:\/\/pyvideo.org\/pycon-italia-2019\/deep-learning-the-final-frontier-for-time-series-analysis-and-signal-processing.html","description":" Deep neural networks are becoming irreplaceable for analyzing most kinds\nof data that humans supposed to exceed in - images, video, sounds,\ntexts. They can not only predict something based on some input but also\ngenerate new images or sounds. Meanwhile, we are forgetting about\nanother very important source of data - signals (or time series, which\nwill be interchangeably here), that gets less hype in public but\nbenefits a lot from applying deep learning comparing to classical\napproaches.<\/p>\n In this talk, we will review what are sources of time series and what\nbusiness goals are we solving while analyzing them, what are \u201cold\u201d tools\nfor analysis and how deep neural nets overcome them, we will learn the\nlatest trends and ruin some myths and, moreover, we will see how\ngenerative models can be applied to the signal processing as well.<\/p>\n After this talk, you\u2019ll be able to boost your current solutions in\nsignal processing or time series analysis with deep learning. It will be\nalso interesting for practitioners in other areas, like computer vision\nor NLP since we will discuss some concepts that are widely applicable.\nPrevious experience with time series is not required, but some\ntheoretical or practical experience with machine learning and\/or neural\nnetworks is preferred.<\/p>\n Feedback form:<\/strong> https:\/\/python.it\/feedback-1716<\/a><\/p>\nDescription<\/h3>