Wolfram, computable document format and what brought into my mind ..

.. what attracted 'me' .. on the cdf .. the computable document format .. Wolfram ..initiative .. and their ..network ..work .. and into what it developed .. this search ..of 'mine' ..

the search .. that goes into acquiring the 'right' expressions .. for the ..'my' ..ideas that keep popping up .. expressions that I need .. in order to give shape ..the oncoming ideas .. dress them up .. with words .. conceptualise the ideas .. into manageable, for my mind concepts .. the culmination of ..attractors' activity and action ..

the ideas .. spawning of .. grappled concepts properties .. seeds ..of properties .. new 'paths' of my brain's neural circuitry .. reverberated cell assemblies .. a new synapse .. the initialization of a neural network .. initialized ..neural network a result of ..bridging .. unabridged, already existing neural networks .. into a ..practically .. and virtually ..new network ..

the new word ..the missed link .. the newly-found expression .. developing into a bridge that connects .. enables the flow of information .. between the previously ..separated networks ..from the virtually near to even disparate regions of my brain's ..neural space

practically ..as already held ..knowledge .. is constantly re-arranged .. from .. slight to larger modifications .. a gradient-like increase .. to accommodate ..newly arrived knowledge ..

towards a full-fledged ..overall ..over-surpassing ..ever-including .. and inclusive ... super- .. or hyper- .. network

Syllabus

This basic course is organized into five segments.

Introduction:

Overview of neural network history and types of problems: function approximation, classification, data clustering, time series, and dynamic systems

Feedforward Neural Networks and Radial Basis Function

Learning, overlearning, and initialization of neural networks

Theory and Background of Neural Networks

Description of the inherited problems when functions are fitted to data, possibilities for handling these problems using neural networks, and practical aspects

Nonlinear Dynamic Black-Box Modeling

Modeling of time series and dynamic systems using linear and nonlinear models

Classification and Clustering with Neural Networks

Two classes, many classes, neural network classifiers and relations to other classifiers, the perceptron as classifier, nearest-neighbor classification, vector quantization, unsupervised methods, self-organizing maps, and the Hopfield network

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