Automatic Differentiation with Theano

Add to your list(s) Download to your calendar using vCal

• Yarin Gal; Christof Angermueller
• Thursday 13 November 2014, 15:00-16:30
• Engineering Department, CBL Room 438.

If you have a question about this talk, please contact mv310.

Implementing models in machine learning and data analysis often requires a considerable effort which can span over weeks. Symbolic differentiation can be used to speed up model prototyping in a large class of models to a single day. In this tutorial we will explain how to use Theano, a Python package for symbolic differentiation. We will walk through the theory and basics of symbolic differentiation, and will implement two real world models: logistic regression and a deep net. A NumPy refresher will be given beforehand.

This talk is part of the Machine Learning Reading Group @ CUED series.

This talk is included in these lists:

• All Talks (aka the CURE list)
• Cambridge Centre for Data-Driven Discovery (C2D3)
• Cambridge Forum of Science and Humanities
• Cambridge Language Sciences
• Cambridge University Engineering Department Talks
• Cambridge talks
• Centre for Smart Infrastructure & Construction
• Chris Davis' list
• Computational Continuum Mechanics Group Seminars
• Engineering Department, CBL Room 438
• Featured lists
• Guy Emerson's list
• Hanchen DaDaDash
• Inference Group Journal Clubs
• Inference Group Summary
• Information Engineering Division seminar list
• Interested Talks
• ML
• Machine Learning Reading Group
• Machine Learning Reading Group @ CUED
• Machine Learning Summary
• Quantum Matter Journal Club
• Required lists for MLG
• School of Technology
• Simon Baker's List
• TQS Journal Clubs
• Trust & Technology Initiative - interesting events
• bld31
• custom
• ndk22's list
• ob366-ai4er
• rp587
• yk373's list

Note that ex-directory lists are not shown.