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Each training example is a pair of the form (2,i ), where x' is the vector of network input values, and is the vector of target network output values q is the learning rate (eg, O5) ni, is the number of network inputs, nhidden the number of units in the hidden layer, and no,, the number of output units The inputfiom unit i into unit j is denoted xji, and the weightfrom unit i to unit j is denoted wji a Create a feed-forward network with ni, inputs, m i d d e n hidden units, and nour output units a Initialize all network weights to small random numbers (eg, between -05 and 5 0) r Until the termination condition is met, Do

The following websites were consulted to prepare this CV: wwwamathnet/perso/MarcGuillemothtm wwwressources-webcom/cv/informatique/BOUCHARDhtm wwwressources-webcom/cv/informatique/CV%20(2)htm wwwressources-webcom/cv/informatique/BEBERIDEhtm

Propagate the input forward through the network:

a fairly advanced group of settings, and it is unlikely that you will need to make any changes here, at least not initially (I have never modified any of these settings).

Propagate the errors backward through the network:

6k 4-

You can use this panel to modify the appearance of the debugger prompt in the console, but it is probably advisable to steer clear of making any changes that you don t really need to make.

ok(l - ok)(tk - 0 k )

I 1 1

Key Bindings refers to the shortcuts you use to access operations within the Xcode environment. These generally follow the norms for Mac OS X software, but there are specific key bindings for Xcode itself. If you have used another coding environment in the past, such as Metrowerks CodeWarrior or BBEdit, you can choose to keep the bindings from that product by choosing it here. Alternatively, you can create your own by duplicating one of the current options and modifying it to meet your needs.

TABLE 42 The stochastic gradient descent version of the BACKPROPAGATION algorithm for feedforward networks containing two layers of sigmoid units

Connaissances Langages de programmations: COBOL Scheme C C++ Pascal ColdFusion Delphi XML VisualBasic JAVA php JavaScript

One major difference in the case of multilayer networks is that the error surface can have multiple local minima, in contrast to the single-minimum parabolic error surface shown in Figure 44 Unfortunately, this means that gradient descent is guaranteed only to converge toward some local minimum, and not necessarily the global minimum error Despite this obstacle, in practice BACKPROPAGATION has been found to produce excellent results in many real-world applications algorithm is presented in Table 42 The algorithm as The BACKPROPAGATION described here applies to layered feedforward networks containing two layers of sigmoid units, with units at each layer connected to all units from the preceding layer This is the incremental, or stochastic, gradient descent version of BACKPROPAGATION The notation used here is the same as that used in earlier sections, with the following extensions:

These preferences govern how the editor works for you. The options are very straightforward and need no any explanation to an audience of this caliber.

An index (eg, an integer) is assigned to each node in the network,where a "node" is either an input to the network or the output of some unit in the network xji denotes the input from node i to unit j , and wji denotes the corresponding weight 6, denotes the error term associated with unit n It plays a role analogous to the quantity (t - o ) in our earlier discussion of the delta training rule As we shall see later, 6, = -

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This preference panel lists all the file types you might expect to be working with in your Xcode software development and allows you to define how those file types are handled. For example, you can specify that image files in your application should be opened with Photoshop, PHP files with TextMate, and so on. In general, my advice with this panel is to leave it with the default mappings unless you have a very good reason not to.