David thank u for the detail explanation.
On 2 February 2015 at 13:25, cogmission1 . <[email protected]> wrote: > Dinesh, > > First of all when you say NuPIC, it helps to be clear about what in fact > that is. NuPIC had a list of components and can be accessed and utilized > from a few different levels of abstraction. There is the OPF (Online > Prediction Framework) which is the "highest" level with which to work. Then > there is the Network API, which allows one to combine components with > specific respect for the job one wants to get done. Then there is working > with the individual components which is the most fine grained and requires > the most technical know-how (most do not deal with this level). > > NuPIC is a framework that in the end delivers a prediction and a > confidence level of that prediction, given a *stream* of input (or sequence > of inputs). It consists of various lower level components. > > 1. A module that allows Swarming (an algorithmic approach to arriving at > (converging on) the best configuration parameters to use with NuPIC. (I > believe it hasn't yet been pulled out so that it can be used independent of > the OPF, but that is a current effort - I may be wrong or not up to date) > > 2. Encoders. Take input of various forms and normalize it across all > possible inputs of a given type to produce a representation which is > suitable for input into other NuPIC components. This input adheres to a few > different constraints. Some of these constraints are configurable such as > how many bits to use for a representation, how many bits within that are to > be "on" bits etc. > > 3. Spatial Pooler. This takes in an encoded bit vector and outputs an SDR > (Sparse Distributed Representation). This SDR is guaranteed to have certain > properties. It also represents other aspects being modeled in a biological > way. This is namely the cortical columns which contain neurons. SDR's have > certain properties such as consistency, resistance to noise (slight > differences in the resultant bit vectors don't have an enormous relative > effect on the semantics) etc. > > 4. Temporal Memory. This, in most cases takes an SDR processed by a > Spatial Pooler - but doesn't have to in all cases depending on the task > being accomplished. This provides the distinctions surrounding prediction > and sequential learning. This also outputs an SDR. > > 5. Classifier. This is used to provide statistical correlation of a > prediction to the input that caused it. It is in most cases added to to the > final layer of processing (but doesn't always have to be used). > > The site at Numenta.org has wikis and links to videos[1][2][3] which > explain and add knowledge about each of these components. This is just a > rough overview, it would help to peruse these and then come back with more > questions which are always welcome :) > > Hope this high level overview helps! > > Welcome aboard! > > David > > 1. http://www.numenta.org > 2. https://github.com/numenta > 3. https://github.com/numenta/nupic/wiki > > On Mon, Feb 2, 2015 at 1:15 AM, Dinesh Deshmukh <[email protected]> > wrote: > >> I would like to know in depth about different modules of nupic.GIve me >> some suggestions of any links that explains how nupic code flows. >> I know abstract view of what htm is but i want to understand at a >> programming level. >> >> What makes nupic about 600MB size? I mean what kind of features it has? >> >> Thank u all. >> > > > > -- > *We find it hard to hear what another is saying because of how loudly "who > one is", speaks...* >
