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SUMMARY:Everything you wanted to know (and more) about PyTorch tensors
DESCRIPTION:Abstract:\n\n\n\nBefore information can be fed to artificial 
 neural networks (ANN)\, it needs to be converted to a form ANN can process: 
 floating point numbers. Indeed\, you don't pass a sentence or an image 
 through an ANN\; you input numbers representing a sequence of words or 
 pixel values.\nAll these floating point numbers need to be stored in a data 
 structure. The most suited structure is multidimensional (to hold several 
 layers of information) and since all data is of the same type\, it is an 
 array.\nPython already has several multidimensional array structures—the 
 most popular of which being NumPy's ndarray—but the particularities of 
 deep learning call for special characteristics: ability to run operations 
 on GPUs and/or in a distributed fashion\, as well as the ability to keep 
 track of computation graphs for automatic differentiation.\nThe PyTorch 
 tensor is a Python data structure with these characteristics that can also 
 easily be converted to/from NumPy's ndarray and integrates well with other 
 Python libraries such as Pandas.\n\nIn this workshop\, suitable for users 
 of all levels\, we will have a deep look at this data structure and go much 
 beyond a basic introduction. In particular\, we will:\n\n\n- see how 
 tensors are stored in memory\n- look at the metadata which allows this 
 efficient memory storage\n- cover the basics of working with tensors 
 (indexing\, vectorized operations...)\n- move tensors to/from GPUs\n- 
 convert tensors to/from NumPy ndarrays\n- see how tensors work in 
 distributed frameworks\n- see how linear algebra can be done with PyTorch 
 tensors\n\n \n\nThings to do before arriving.\n\nPrerequisites:\n\n\n\nIf 
 you want to follow along the hands-on part of this workshop (which is 
 totally up to you)\, you will need up to date versions of:\n\n- Python\n\n- 
 PyTorch (see https://pytorch.org/get-started/locally/ for 
 installation)\n\n- a terminal emulator (Linux and MacOS probably already 
 have one\, Windows users can install the free version of MobaXterm (see 
 https://mobaxterm.mobatek.net/download.html for 
 installation)\n\n\n\nBecause we will cover a lot of material\, the pace 
 will be brisky without much time for troubleshooting\, so you might also 
 choose to simply watch and practice at your own pace after the 
 workshop.\n\n\n\n---\n\nAbout the presenters:\n\n\n\nMarie-Helene Burle 
 Prior to entering the realm of computing\, Marie-Helene Burle spent 15 
 years roaming the globe from the High Arctic to uninhabited Sub-Antarctic 
 islands or desert tropical atolls\, conducting bird and mammal research 
 (she calls those her "years running after penguins"). As a PhD candidate in 
 behavioural and evolutionary biology at Simon Fraser University\, she 
 "fell" into Emacs\, R\, and Linux. This turned Marie into an advocate for 
 open source tools and improved computing literacy for all\, as well as 
 better coding practices and more reproducible workflows in science. She 
 started to contribute to the open source community\, became a Software and 
 Data Carpentry Instructor\, and worked at the SFU Research Commons 
 providing programming support to researchers. She is thrilled to be 
 continuing in this direction with HPC and new languages at WestGrid. When 
 not behind a computer\, Marie loves reading history books and looking for 
 powder in the British Columbia backcountry on skis.\n\n---\n\nLocation 
 Details\n\n\n	Location:\n	*ONLINE*\n\n\nIf you have any questions\, 
 concerns\, or accessibility needs please email 
 [eugene.barsky@ubc.ca].\n\nTo keep up-to-date with all of the workshops\, 
 consults\, and events subscribe to the UBC Library Research Commons monthly 
 newsletter.\n\nThis event is online. Registrants receive the link 24 hours 
 before the event. Registration closes at the same time.
ORGANIZER;CN="Eugene Barsky":MAILTO:eugene.barsky@ubc.ca
CATEGORIES:Data, Digital Scholarship, Research Commons, Research Data Management
CONTACT;CN="Eugene Barsky":MAILTO:eugene.barsky@ubc.ca
STATUS:CONFIRMED
UID:LibCal-3653468
URL:https://libcal.library.ubc.ca/event/3653468
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