onsiderable progress has been made in speech-recognition technology over the last few years and nowhere has this progress been more evident than in the
area of large-vocabularyrecognition (LVR). Current laboratory systems are capable of transcribing conti
Department of Computer Science
University of Toronto
http:/learning.cs.toronto.edu
6 Kings College Rd, Toronto
M5S 3G4, Canada
fax: +1 416 978 1455
c
Copyright Georey Hinton 2010.
August 2, 2010
UTML TR 2010003
A Practical Guide to Training
Restricted Bol
1
Learning Deep Architectures for AI
Yoshua Bengio
Dept. IRO, Universit de Montr al
e
e
C.P. 6128, Montreal, Qc, H3C 3J7, Canada
[email protected]
http:/www.iro.umontreal.ca/bengioy
To appear in Foundations and Trends in Machine Learning
Abstract
13 The Hopeld Model
One of the milestones for the current renaissance in the eld of neural networks was the associative model proposed by Hopeld at the beginning of the 1980s. Hopelds approach illustrates the way theoretical physicists like to think about
Speech Recognition and HMM Learning
l
Overview of speech recognition approaches
Standard Bayesian Model
Features
Acoustic Model Approaches
Language Model
Decoder
Issues
l
Hidden Markov Models
HMM Basics
HMM in Speech
l
Forward, Backward, and Viter
Relaxation and Hopfield Networks
Neural Networks
Neural Networks - Hopfield
Bibliography
Hopfield, J. J., "Neural networks and physical systems with emergent
collective computational abilities," Proceedings of the National Academy of
Sciences 79:2554-2558
1
A
V
0.9
0.8
0.7
0.6
B
C
0.5
0.4
0.3
0.2
A: sigmoid
B: EBA (x0=0.03)
0.1
0
-0.1
C: EBA (x0=0.05)
-0.05
0
U
0.05
0.1
100
80
60
A: CR with EBA
B: CR without EBA
C: nither CR nor EBA
A
B
10
40
20
0
8
C
0.04 0.06 0.08 0.1 0.12 0.14 0.16
R
Error(%)
Valid Tour
Mach Learn (2006) 63:183205
DOI 10.1007/s10994-006-6266-6
Classication-based objective functions
Michael Rimer Tony Martinez
Received: 3 June 2005 / Revised: 4 November 2005 / Accepted: 11 November 2005 /
Published online: 3 March 2006
Springer Science +
An Introduction to
the Conjugate Gradient Method
Without the Agonizing Pain
Edition 1 1
4
Jonathan Richard Shewchuk
August 4, 1994
School of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213
Abstract
The Conjugate Gradient Method is the mos
Reinforcement Learning
Variation on Supervised Learning
Exact target outputs are not given
Some variation of reward is given either immediately or after some
steps
Chess
Path Discovery
RL systems learn a mapping from states to actions by trial-and-error
A guide to recurrent neural networks and backpropagation
Mikael Bodn
e
[email protected]
School of Information Science, Computer and Electrical Engineering
Halmstad University.
November 13, 2001
Abstract
This paper provides guidance to some of the co
Support Vector Machines
Elegant combination of statistical learning
theory and machine learning Vapnik
Good empirical results
Non-trivial implementation
Can be slow and memory intensive
Binary classifier
Much current work
SVM Comparisons
In order to have
Deep Learning
l
l
l
l
Early Work
Why Deep Learning
Stacked Auto Encoders
Deep Belief Networks
CS 678 Deep Learning
1
Deep Learning Overview
l
l
Train networks with many layers (vs. shallow nets with
just a couple of layers)
Multiple layers work to build a