Open RahulBaboota opened 5 years ago
Thank you for your proposal @RahulBaboota I think it is generally good. You might want to update the gifs to have higher resolution. Also, your slides are outdated - you should update the slide deck. For instance, variable
is deprecated now :)
Best of luck with the talk!
Hi @RahulBaboota, have you updated the slides?
Hi @MSanKeys963, I have updated the slides.
Please have a look @shagunsodhani.
The slides look quite good. Just one point, there are still some references to Variable
. For example https://slides.com/rahulbaboota/deck#/7/1 I would recommend removing them.
Best of luck with the talk!
I am sorry for the typo. I have updated the relevant slides.
Abstract (2-3 lines) This talk aims to introduce Facebook's Deep Learning library - PyTorch. PyTorch is increasing becoming popular due to it's powerful features and shallow learning curve, making it accessible and easy to use to a larger community. This talk discusses about the 'Autograd' package, which is central to all neural networks in PyTorch. It also entails details about the different features and functionalities of PyTorch as well as equip the audience on how to create simple and complex Neural Networks in PyTorch. PyTorch helps to create dynamic computation graphs that allow you to change how the network behaves on the fly unlike static computation graphs. It offers modularity which enhances the ability to debug or see within the network.
Brief Description and Contents to be covered The talk will be broadly divided into 2 broad parts.
Part 1 will be an Introduction to PyTorch. This part will focus on the use and need for PyTorch as a deep learning framework. This will be followed by instructions on how to setup PyTorch and a look at the basic building blocks behind the framework.
Part 2 will dive more into the features of PyTorch, mainly it's AutoGrad package which lies at the heart of all Neural Networks created in PyTorch and PyTorch's ability to create dynamic computational graphs as opposed to the static computational graphs offered by some of it's counterparts (such as TensorFlow and Caffe).
Pre-requisites for the talk
Time required for the talk 30 minutes
Link to slides https://slides.com/rahulbaboota/deck
Will you be doing hands-on demo as well?
Link to ipython notebook (if any)
About yourself I am Rahul Baboota, a 4th Year Undergraduate in India studying Computer Science and Engineering. I have an avid interest in the domain of Data Science, Machine Learning and Deep Learning. I have worked at various Data Science and Machine Learning based startups and labs. In my freshmen year, I worked at a data journalism startup to create and analyze smart data stories. I was also a part of a project funded by the Government of India for the development of a social media based analytics tool for the analysis of healthcare and nutrition in India. I am currently working at the Center for Artificial Intelligence at IIITD in the Autonomous Vehicle Lab 'Swarath'.
Are you comfortable if the talk is recorded and uploaded to PyData Delhi's YouTube channel ? Yes
Any query ?