ML Classification Bundling for HIM & Her Connection specifics for a better brain; classified by type & example: Human Brain cells have 1000 connections, squid 10000; Each connection does:

ML Classification Bundling for HIM & Her Connection specifics for a better brain; classified by type & example: Human Brain cells have 1000 connections, squid 10000; Each connection does:

[Duke Abbaddon]

ML Classification Bundling for HIM & Her

Connection specifics for a better brain; classified by type & example:

Human Brain cells have 1000 connections, squid 10000; Each connection does:

7Bit regular
8Bit, sharp
9Bit on better effort,
10Bit on clarity & meditation + work hard
6Bit on relaxed,
5Bit on drunk

Connections for dedicated skills such as maths have:

Dedication bundling (multiple connections)
Multiple Affirmations, A-Synchronous, Synchronous

1 5Bit to 7Bit
2, 5Bit to 18Bit
3, 7Bit to 26Bit
4, 16Bit to 38Bit
5, 17Bit to 48Bit

Eyes for example can bundle 5 on training, colour purity..
lower bundling offers more flexibility,
High bundling offers assurety & speed & retention.

RS

Python & JS Configurations
https://is.gd/DictionarySortJS

https://science.n-helix.com/2021/03/brain-bit-precision-int32-fp32-int16.html
https://science.n-helix.com/2022/10/ml.html

*********************

Brain Bit Precision Int32 FP32, Int16 FP16, Int8 FP8, Int6 FP6, Int4?
Idealness of Computational Machine Learning ML TOPS for the human
brain:

Brain level Int/Float inferencing is ideally in Int8/7 with error bits
or float remainders

Comparison List : RS

48Bit Int+Float Int48+FP48 (many connections, Eyes for example) HDR Vison

40BitInt+Float Int40+FP40 HDR Basic

Int16 FP32

Int8 Float16(2Channel, Brain Node)(3% Brain Study)

Int7 (20% Brain Study)

Int6 (80% Brain Study)

Int5 (Wolves (some are 6+))

Int4 (Sheep & worms)

Int3 (Germ biosystems)

Statistically a science test stated 80% of brains in man quantify Bit
at 6 20% to 7Bit

XBox X & PlayStation 5 do down to INT4Bit (quite likely for quick inferencing)

Be aware that using 4 bit Int instructions .. potentially means more
instructions used per clock cycle & more micro data transfers..

Int8 is most commonly liable to quantify data with minimum error in
8Bit like the Atari STE or the Nintendo 8Bit..

Colour perception for example is many orders of magnitude higher! Or
8bit colours EGA is all we would use..

16Bit was not good enough.. But 32Bit suites most people! But
10Bit(x4) 40Bit & Dolby 12Bit(x4) 48Bit is a luxury & we love it!

Precision Quality Control in ML:

While nothing is sure, Human beings appear to have Integer of around 8
& are more surely able to practice Float units,

Bundling is when multiple Neuron roots go to the same neuron in Sync
from the same response cluster Neurons,

This feature enhances data integrity & precision by multiplying data
transfer & response precision..

Eye Neurons are an example & so are feelings from clustered neurons
such as hands, feet & the sensory organs,

Memory & Maths calculations.

(c)Rupert S https://is.gd/ProcessorLasso

https://science.n-helix.com/2021/03/brain-bit-precision-int32-fp32-int16.html
https://science.n-helix.com/2022/10/ml.html

ML Classification Bundling for HIM & Her

Connection specifics for a better brain; classified by type & example:

Human Brain cells have 1000 connections, squid 10000; Each connection does:

7Bit regular
8Bit, sharp
9Bit on better effort,
10Bit on clarity & meditation + work hard
6Bit on relaxed,
5Bit on drunk

Connections for dedicated skills such as maths have:

Dedication bundling (multiple connections)
Multiple Affirmations, A-Synchronous, Synchronous

1 5Bit to 7Bit
2, 5Bit to 18Bit
3, 7Bit to 26Bit
4, 16Bit to 38Bit
5, 17Bit to 48Bit

Eyes for example can bundle 5 on training, colour purity..
lower bundling offers more flexibility,
High bundling offers assurety & speed & retention.

RS

Python & JS Configurations
https://is.gd/DictionarySortJS

Restricted Boltzmann ML Networks : Brain Efficient

I propose that SIMD of large scale width & depth can implement the model :
Restricted Boltzmann Machines (RBMs) have been proposed for developing
neural networks for a variety of unsupervised machine learning
applications

Restricted Boltzmann Machines utilize a percentage correctness based
upon energy levels of multiple node values; That represent a
percentage chance of a correct solution,

My impression is that Annealer machine simply utilise more hidden
values per node on a neural network,
Thus i propose that SIMD of large scale width & depth can implement the model..

A flexible approach is to experiment with percentages from a base value...
100 or 1000; We can therefore attempt to work with percentiles in
order to adapt classical computation to the theory of multiplicity.

SiMD in parallel can; As we know with RISC Architecture ..
Attempt to run an ideal network composing many times Factor &
regression learning model..

Once the rules are set; Millions of independent IO OPS can be
performed in cyclic learning,

Without sending or receiving data in a way that interferes with the
main CPU & GPU Function..

Localised DMA.

Adaptive hyperparameter updating for training restricted Boltzmann machines on:
Quantum annealers
Wide Path SiMD

"Adaptive hyperparameter updating for training restricted Boltzmann
machines on quantum annealers"

https://www.nature.com/articles/s41598-021-82197-1.pdf

https://www.nature.com/articles/s41598-021-82197-1

https://science.n-helix.com/2019/06/vulkan-stack.html

"Restricted Boltzmann Machines (RBMs) have been proposed for
developing neural networks for a
variety of unsupervised machine learning applications such as image
recognition, drug discovery,
and materials design. The Boltzmann probability distribution is used
as a model to identify network
parameters by optimizing the likelihood of predicting an output given
hidden states trained on
available data. Training such networks often requires sampling over a
large probability space that
must be approximated during gradient based optimization. Quantum
annealing has been proposed
as a means to search this space more efficiently which has been
experimentally investigated on
D-Wave hardware. D-Wave implementation requires selection of an
effective inverse temperature
or hyperparameter (β) within the Boltzmann distribution which can
strongly influence optimization.
Here, we show how this parameter can be estimated as a hyperparameter
applied to D-Wave
hardware during neural network training by maximizing the likelihood
or minimizing the Shannon
entropy. We find both methods improve training RBMs based upon D-Wave
hardware experimental
validation on an image recognition problem. Neural network image
reconstruction errors are
evaluated using Bayesian uncertainty analysis which illustrate more
than an order magnitude
lower image reconstruction error using the maximum likelihood over
manually optimizing the
hyperparameter. The maximum likelihood method is also shown to
out-perform minimizing the
Shannon entropy for image reconstruction."

(c)Rupert S https:/science.n-helix.com

Example ML Statistic Variable Conversion : Super Sampling Virtual
Resolutions : Talking about machine learning & Hardware functions to
use it/Run it;  To run within the SiMD & AVX feature-set.

For example this works well with fonts & web browsers & consoles or
standard input display hubs or User Interfaces, UI & JS & Webpage
code.

In the old days photo applications did exist to use ML Image
enhancement on older processors..
So how do they exploit Machine Learning on hardware with MMX for example ?

Procedural process data analytics:

Converting large statistics data bases; On general
Tessellation/Interpolation of images
The procedural element is writing the code that interpolates data
based upon the statistics database...

Associated colours..
Face identity...
Linearity or curvature...
Association of grain & texture...

Databases get large fast & a 2 MB to 15MB Database makes the most sense...
Averages have to be categorized by either being worthy of 2 Places in
the database or an average..

You can still run ML on a database object & then the points in the
table are called nodes!

Indeed you can do both, However database conversion makes datasets way
more manageable to run within the SiMD & AVX feature-set.

However the matter of inferencing then has to be reduced to
statistical averages & sometimes ML runs fine inferencing this way.

Both ways work, Whatever is best for you & the specific hardware.

(c)Rupert S

DL-ML slide : Machine Learning DL-ML

By my logic the implementation of a CPU+GPU model would be fluid to both..

Machine Learning : Scientific details relevant to DL-ML slide
(CPU,GPU,SiMD Hash table(M1 Vector Matrix-table +Speed)

The vector logic is compatible to both CPU+GPU+SiMD+AVX.

Relevant because we use Vector Matrix Table hardware.. and in notes
the Matrix significantly speeds up the process.
(Quantum Light Matrix)

The relevance to us is immense with world VM servers
DL-ML Machine Learning Model compatible with our hardware
By my logic the implementation of a CPU+GPU model would be fluid to both..
The vector logic is compatible with both CPU+GPU.

However this is a model we can use & train..
For common core : Rupert S https://is.gd/ProcessorLasso

https://www.marktechpost.com/2021/04/10/computer-scientists-from-rice-university-display-cpu-algorithm-that-trains-deep-neural-networks-15-times-faster-than-gpu/

https://arxiv.org/pdf/2103.10891.pdf

"State-of-the-art approaches such as OpenMP and OpenCL"
https://is.gd/LEDSource

High performance ML Versatile Compute chips
https://www.xilinx.com/content/dam/xilinx/support/documents/data_sheets/ds950-versal-overview.pdf

https://science.n-helix.com/2022/10/ml.html
https://science.n-helix.com/2021/03/brain-bit-precision-int32-fp32-int16.html

https://science.n-helix.com/2023/06/tops.html

machine learning https://www.amazon.com/dp/B08V134ZFD

Tokma ML

Python & JS Configurations
https://is.gd/DictionarySortJS

https://iopscience.iop.org/article/10.1088/1741-4326/ad142f

https://is.gd/TokmaML