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Each Linux distribution that bases its software on RPM files will compile the software distribution for you as well as release source RPM packages. A source RPM package is an RPM containing the source code for the software as well as instructions on how to prepare and compile the software. The RPM will also contain the spec file. SUSE uses the auto-build system to automate the process of compiling the same source RPM files to create binary RPMs for all the supported architectures (Intel 32/64, AMD64, PPC, and zSeries). This provides SUSE with an advantage over the other distributions in that all architectures are based on exactly the same source base. This means that you can use Linux on a desktop for development work and there is a 99 percent chance that you can recompile and run on a mainframe because all the libraries are the same, from the same code base.

A large proportion of source code actually includes a spec file in the source distribution, which means you can compile into an RPM and use software versioning to control the installation. An RPM package is only as good as the person who created it. All of the intelligence that you saw in RPM earlier in the chapter is controlled by how the RPM is created. Earlier, we talked briefly about spec files and how they are used to control package creation. We will now create an RPM from a small program and show how it all fits together.

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You will create a spec file later in the chapter, but first you need to see how the process works. SUSE uses the directory /usr/src/packages to build and create an RPM package. This directory contains subdirectories, and Table 12-2 shows the reasoning behind this.

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1 Give an expression for ok,p for a FFNN with direct connections between the input and output layer 2 Why is the term (-l)^'+i possible in equation (34) 3 Explain what is meant by the terms overfitting and underfitting Why is v > v + av a valid indication of overfitting 4 Investigate the following aspects: (a) Are direct connections between the input and output layers advantageous Give experimental results to illustrate (b) Compare a FFNN and an Elman RNN trained using GD Use the following function as benchmark: zt = 1 + 03z t-2 14z2t-1, with 1,22 ~ U( 1,1), sampled from a uniform distribution in the range ( 1,1) (c) Compare stochastic learning and batch learning using GD for the function ot = zt where zt = 03^_6 062^-4 + 05^_i + 03^|_6 0.

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Directory BUILD SOURCES SPECS SRPMS RPMS Description This is where your original source code is unpacked and compiled. A placeholder for source files before they are unpacked. Contains the spec files for the package you are creating. If you are creating source RPMS, they are stored here in source RPM once built. The final resting place for your packaged RPM. This directory is further subdivided into architecture type (i386, i586, i686, and so on).

The architecture of an RPM package can be a subset of a general architecture; for example, Intel can be i386 (386-based, works on all Intel-based machines). The i586/i686/Athlon architectures will usually contain specific optimizations for the higher Intel/AMD processors.

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To transform source code (C, C++, Fortran, and so on) to a machine architecture binary, you use a compiler. A compiler takes the source code and analyzes it for syntactical and semantic errors (much like checking the spelling and grammar of text), tokenizing (splitting the source code into smaller, quantifiable chunks), and then producing a binary run time. This run time is linked to any libraries it relies upon to run. The internals of these operations are handled for the C programming language by gcc (GNU C Compiler). It is unlikely you will have to use gcc directly unless you want to produce applications in Linux (or any other Unix), but it is useful to know it exists and is the main contributor to the creation of Linux applications. If you wrote the standard Hello World! application in C:

2z^_4 + <^, and zt ~ U( l, 1) for t 1, , 10, and C^t ~ AT(0,005) is zero-mean noise sampled from a normal distribution (d) Compare GD and SCG on any classification problem from the UCI repository at http://wwwicsuciedu/ "mlearn/MLRepositoryhtml (e) Design and perform an experiment to illustrate if PSO yields better performance than GD 5 Assume gradient descent is used as optimization algorithm, and derive the learning equations for the Elman SRNN, the Jordan SRNN, TDNN and FLNN 6 Explain how a SRNN learns the temporal characteristics of data 7 How can a FLNN be used to improve accuracy and training speed 8 Explain why bias for only the output units of a PUNN, as discussed in this chapter, is sufficient 9 Discuss the effect of having an augmented input as part of the product calculation of the net input signal 10.

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#include <stdio.h> int main () { printf ( Hello World!\n ); }

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