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deep learning 教程Andrew英文原版.pdf

deep

learning

Andrew

英文原版

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deep learning 教程 Andrew 英文原版，熟悉英文的可以看这个。

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13-3-22 U F LD L Tutorial - U fldl
1/3deeplearning.stanford.edu/w iki/index.php/U F LD L_Tutorial
UFLDL Tutorial
From Ufldl
Description: This tutorial will teach you the main ideas of Unsupervised Feature
Learning and Deep Learning. By working through it, you will also get to implement
several feature learning/deep learning algorithms, get to see them work for
yourself, and learn how to apply/adapt these ideas to new problems.
This tutorial assumes a basic knowledge of machine learning (specifically,
familiarity with the ideas of supervised learning, logistic regression, gradient
descent). If you are not familiar with these ideas, we suggest you go to this
Machine Learning course
(http://openclassroom.stanford.edu/MainFolder/CoursePage.php?course=MachineLearning)
and complete sections II, III, IV (up to Logistic Regression) first.
Sparse Autoencoder
Neural Networks
Backpropagation Algorithm
Gradient checking and advanced optimization
Autoencoders and Sparsity
Visualizing a Trained Autoencoder
Sparse Autoencoder Notation Summary
Exercise:Sparse Autoencoder
Vectorized implementation
Vectorization
Logistic Regression Vectorization Example
Neural Network Vectorization
Exercise:Vectorization
Preprocessing: PCA and Whitening
PCA
Whitening
Implementing PCA/Whitening
Exercise:PCA in 2D
Exercise:PCA and Whitening
Softmax Regression
Softmax Regression
Exercise:Softmax Regression

13-3-22 U F LD L Tutorial - U fldl
2/3deeplearning.stanford.edu/w iki/index.php/U F LD L_Tutorial
Self-Taught Learning and Unsupervised Feature Learning
Self-Taught Learning
Exercise:Self-Taught Learning
Building Deep Networks for Classification
From Self-Taught Learning to Deep Networks
Deep Networks: Overview
Stacked Autoencoders
Fine-tuning Stacked AEs
Exercise: Implement deep networks for digit classification
Linear Decoders with Autoencoders
Linear Decoders
Exercise:Learning color features with Sparse Autoencoders
Working with Large Images
Feature extraction using convolution
Pooling
Exercise:Convolution and Pooling
Note: The sections above this line are stable. The sections below are still under
construction, and may change without notice. Feel free to browse around however, and
feedback/suggestions are welcome.
Miscellaneous
MATLAB Modules
Style Guide
Useful Links
Miscellaneous Topics
Data Preprocessing
Deriving gradients using the backpropagation idea
Advanced Topics:
Sparse Coding
Sparse Coding
Sparse Coding: Autoencoder Interpretation
Exercise:Sparse Coding
ICA Style Models
Independent Component Analysis
Exercise:Independent Component Analysis

13-3-22 U F LD L Tutorial - U fldl

3/3deeplearning.stanford.edu/w iki/index.php/U F LD L_Tutorial

Others

Convolutional training

Restricted Boltzmann Machines

Deep Belief Networks

Denoising Autoencoders

K-means

Spatial pyramids / Multiscale

Slow Feature Analysis

Tiled Convolution Networks

Material contributed by: Andrew Ng, Jiquan Ngiam, Chuan Yu Foo, Yifan Mai, Caroline

Suen

Retrieved from "http://deeplearning.stanford.edu/wiki/index.php/UFLDL_Tutorial"

This page was last modified on 20 October 2011, at 01:28.

13-3-22 N eural N etw orks - U fldl

1/5deeplearning.stanford.edu/w iki/index.php/N eural_N etw orks

Neural Networks

From Ufldl

Consider a supervised learning problem where we have access to labeled training

examples (x

(i)

). Neural networks give a way of defining a complex, non-linear

form of hypotheses h

W,b

(x), with parameters W,b that we can fit to our data.

To describe neural networks, we will begin by describing the simplest possible

neural network, one which comprises a single "neuron." We will use the following

diagram to denote a single neuron:

This "neuron" is a computational unit that takes as input x

(and a +1

intercept term), and outputs , where

is called the activation function. In these notes, we will choose

to be the sigmoid function:

Thus, our single neuron corresponds exactly to the input-output mapping defined by

logistic regression.

Although these notes will use the sigmoid function, it is worth noting that another

common choice for f is the hyperbolic tangent, or tanh, function:

Here are plots of the sigmoid and tanh functions:

13-3-22 N eural N etw orks - U fldl

2/5deeplearning.stanford.edu/w iki/index.php/N eural_N etw orks

The tanh(z) function is a rescaled version of the sigmoid, and its output range is [

− 1,1] instead of [0,1].

Note that unlike some other venues (including the OpenClassroom videos, and parts of

CS229), we are not using the convention here of x

= 1. Instead, the intercept term

is handled separately by the parameter b.

Finally, one identity that'll be useful later: If f(z) = 1 / (1 + exp( − z)) is the

sigmoid function, then its derivative is given by f'(z) = f(z)(1 − f(z)). (If f is

the tanh function, then its derivative is given by f'(z) = 1 − (f(z))

.) You can

derive this yourself using the definition of the sigmoid (or tanh) function.

Neural Network model

A neural network is put together by hooking together many of our simple "neurons,"

so that the output of a neuron can be the input of another. For example, here is a

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2014-06-14

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