Machine Learning New Math – Deep Layer Linear Algebra. Solve problems in reduced coeffient space. Since quadratic matrices are too large. Represent the problem in two or three layers with reduction of parameters. // WOW Per
Machine Learning Complex Numbers guess – How many layers does it take to represent complex numbers? layer1 x = sqrt(-1) to y representation in layer2. One layer number can represent complex if its quadratic. Then two layers to reduce the coeffients greatly. x = model(X) and x->X.
Machine Learning – 3 layer Fourier Series Coeffients. Since you can iterate 1 sample with a perceptron of 3 layers to get the x = X. A fourier version with 3 layers can be developed. Not just one layer of complex coeffients. Could complex coffient vector be 2 non linear layers?
#Covid19 interesting home treatment – Gargle with small pieces of bana inside peel scrapings. The idea is that the inside of the banana peel protects the banana. Anti virus? Gargle the cold water with only small pieces of inside banana scrapings. Could inspire covid cure?
Lorenz Chaos Equation – I got more chaos from Lorenz differential equation. Setting an inital value for rho = 28 then 0 and oscillating the system to 0 and giving life to the derivate at sin(t)<0.9 gives // Per
Its a super star not a black hole. Stars calculate the differential equations for objects orbiting them. Black holes calculate the chaotic differential eqautions. Too much random and you need a black hole. KMeans() for the number of objects in its responsiblity // Per
Machine Learning Math – Data efficiency – Is there such a thing as data efficiency? Extract the right data for efficient calculations. Better accuracy. Like getting the intercept points in a fourier sinwave seperation of the data. // Per
Machine Learning Classification of Problem Solving in Temperature degree idea – My idea is that levels of temperatures in a solution. From gravitational to minus to plus. Gravitational in that it can take more input before overheating. //Per
#Covid19 – Food body chemistry – Fool the virus. Create a 3.5 day distorsion to cancel out its collaborative decision making system. Maybe its an idea to have different foods at the hospital so the patient themselves can select. This way you get a random distribution distorsion.
Major Job Opportunities for young people – Resellers collaborate with the library to temperory loan out tech products and others. Youtubers could make a 2 weeks loan on it to do a video. Earn money for themselves and the resellers. Library could be a job creator. Wow tech // Per
You can calculate everything with machine learning as long as you know a relevant truth. Just to do experiments that the matrix function model then has to adapt to. Assume all
input data can affect the virus. Then you insert a truth in the process that becomes label = 1 and without label = 0. Then train the model so that it grasps when something has been
inserted. It corresponds to 0.0.93,1.22, -0.01,1.01,1,0.1,0 from random mix of data. Remove the manual deposit and see what the model classifies for similar problems.
Idea – You can measure magnetic field noise and the time it takes for the virus to die in a hot water saline solution and in a hot water solution. It will be label 1 for saline and label 0 for hot water only. Collect a lot of data X_train and y_train = 0,0,0, … 1,1,1 … which is a label truth> 1 min. Randomly mix data and then practice the ML function. Then run the model on any substance in water to calculate the probability it takes before the virus dies.
Quantum Computer = Function(raspberrypi) – guess – Noise is music to atoms. So different atoms will respond differently to different noise. Assume correct results means the atom liked the noise. Classify different noise functions to different atom # id’s. Then use only good fun.
Covid19 – Food – Bread makers. Make a cheap design that does ?not require yiest. Solved with ?USB-c power. Pack the most food in a flower package + water. Solve food crises with electrical bread makers now. Its that simple. #China#USA#EU#Africa
Math guess – If all points in a graph are true you don’t do a linear approximation function. Since its a line between two points that is the thing. Draw other lines so you don’t need to change the point locations. Then what properties? All multi line coeffients are define trusted
Black hole information paradox – If an object is digitialized in 0’s and 1’s you can imagine the logic of super position travel. If a particle changes between 1 and 0 you can do inf. particle add(a,b) when its 0 since the result is 0. So you need particle memory and 0 capability.