UFLDL Tutorial

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Jogos For Newbies
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'''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.
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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 [http://openclassroom.stanford.edu/MainFolder/CoursePage.php?course=MachineLearning Machine Learning course] and complete
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sections II, III, IV (up to Logistic Regression) first.
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To begin with and foremost you'll need a superb table. There are several poker tables to choose from; you simply must know in which to search. The ideal area to search is on the internet actually. That is mainly because you can find so many alternatives to create and countless shops to choose from. Some tables have seating for five whilst other individuals have seating as much as ten. It truly is dependent on how massive your games often are. There are also convertible sort poker tables that double being a eating area table by eliminating the top part. On line shops present all kinds of tables at great selling prices. Just be sure you get one that is made from reliable wooden. You don't want your table to interrupt below the strain of people leaning over the table.To know more about[http://poker.co.pt/ jogos de poker]
 
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I is not going to get in to the specific policies of playing poker but I can let you know that only two gamers are necessary to bet for each spherical although another eight can view their initially two cards with no risking a cent. My game of alternative is Texas Hold'em, the current craze throughout the country and one that excites me once i am within the setting. The two people required to wager signify the big and compact blinds. If you're the seller or anoy other people for the table, you'll be able to perspective your first two cards totally free not having an bet. In case the hand is weak, you are able to fold and keep your gambling stake.
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'''Sparse Autoencoder'''
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* [[Neural Networks]]
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* [[Backpropagation Algorithm]]
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* [[Gradient checking and advanced optimization]]
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* [[Autoencoders and Sparsity]]
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* [[Visualizing a Trained Autoencoder]]
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* [[Sparse Autoencoder Notation Summary]]
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* [[Exercise:Sparse Autoencoder]]
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All gambling gameswhich might be staying played for the bars and casinos will also be played from the Jogos on-line. Poker, Baccarat, Roulette, Craps, Backgammon and Blackjack are few with the games which were currently being played usually both equally on the web as well as in pubs. The Jogos which might be played on line fall below 3 important types. The initial category consists of the usual card video games, that are played working with cards.To know more about[http://en.wikipedia.org/wiki/List_of_poker_hands List of poker hands]
 
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I played at a WSOP No Restrict Holdem function by using a $1500 get in this particular 12 months. The first day, inside a field of 2600 entrants, I played from twelve:00 noon, until eventually twelve:30am and busted out just previous to the concludeof the day about twenty sites just before I'd have manufactured the cash and gotten paid out! Naturally, there were breaks each and every several hrs and 45 minutes for dinner inside the center, but a much less glamorous issue to complete, I can't envision. To not mention the fact that you will be cramped into a little chair at a table with 8 or nine other guys who may perhaps or might not consider up their honest reveal of area. It will get ancient!
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'''Vectorized implementation'''
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* [[Vectorization]]
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* [[Logistic Regression Vectorization Example]]
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* [[Neural Network Vectorization]]
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* [[Exercise:Vectorization]]
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A pro will even need to be pretty sensible about his bankroll and the way he utilizes it. A poker pro means that he has to dwell off his winnings only. Poker is usually a pretty superior variance recreation so one month a professional could make very little. It really is essential that a professional has himself coated for no less than six months in advance ought to nearly anything go improper.
 
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If you're new to your earth of on line poker you will discover almost certainly a couple of things you will need to learn to acquire you with your solution to growing to be a successful player. Finding out to perform poker is just the beginning, you will find even now a lengthy strategy to go if your preparing on getting a worthwhile player, but it surely could be less difficult than many people may believe. It can be only a make a difference of taking time and effort to learn.To know more about[http://www.titan-poker-coupon-code.info/ Titan Poker Coupon Code]
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'''Preprocessing: PCA and Whitening'''
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* [[PCA]]
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* [[Whitening]]
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* [[Implementing PCA/Whitening]]
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* [[Exercise:PCA in 2D]]
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* [[Exercise:PCA and Whitening]]
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'''Softmax Regression'''
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* [[Softmax Regression]]
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* [[Exercise:Softmax Regression]]
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'''Self-Taught Learning and Unsupervised Feature Learning'''
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* [[Self-Taught Learning]]
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* [[Exercise:Self-Taught Learning]]
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'''Building Deep Networks for Classification'''
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* [[Self-Taught Learning to Deep Networks | From Self-Taught Learning to Deep Networks]]
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* [[Deep Networks: Overview]]
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* [[Stacked Autoencoders]]
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* [[Fine-tuning Stacked AEs]]
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* [[Exercise: Implement deep networks for digit classification]]
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'''Linear Decoders with Autoencoders'''
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* [[Linear Decoders]]
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* [[Exercise:Learning color features with Sparse Autoencoders]]
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'''Working with Large Images'''
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* [[Feature extraction using convolution]]
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* [[Pooling]]
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* [[Exercise:Convolution and Pooling]]
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'''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.  
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'''Miscellaneous'''
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* [[MATLAB Modules]]
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* [[Style Guide]]
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* [[Useful Links]]
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'''Miscellaneous Topics'''
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* [[Data Preprocessing]]
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* [[Deriving gradients using the backpropagation idea]]
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'''Advanced Topics''':
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'''Sparse Coding'''
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* [[Sparse Coding]]
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* [[Sparse Coding: Autoencoder Interpretation]]
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* [[Exercise:Sparse Coding]]
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'''ICA Style Models'''
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* [[Independent Component Analysis]]
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* [[Exercise:Independent Component Analysis]]
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'''Others'''
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* [[Convolutional training]]
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* [[Restricted Boltzmann Machines]]
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* [[Deep Belief Networks]]
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* [[Denoising Autoencoders]]
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* [[K-means]]
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* [[Spatial pyramids / Multiscale]]
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* [[Slow Feature Analysis]]
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* [[Tiled Convolution Networks]]
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Material contributed by: Andrew Ng, Jiquan Ngiam, Chuan Yu Foo, Yifan Mai, Caroline Suen

Revision as of 13:37, 8 September 2011

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