Beginner Music Innovation – Software + Hardware keyboard toy like synth powered by a raspberry pi. Built in speakers. Wifi synth for phone control. Wow. Beginner price 40$

Beginner Music Innovation – Software + Hardware keyboard toy like synth powered by a raspberry pi. Built in speakers. Wifi synth for phone control. Wow. Beginner price 40$

Raspberry Pi old innovation – Real size C64 or real size Amiga 1200 case with keyboard with a raspberry pi zero inside. Boot directly into different fantasy computers. Which programming language do you prefer? Could be made for 30$ magic price.

Gravity Insight guess – Maybe the Saturnus rings has something to do with gravity on Earth being a gravity dominated planet. Obviously. So gravity and magnetism belongs somehow since its a closed loop that forces the earth in its position. // Per Like magnetism a closed loop ring. Hmm. Sounds like a force is created when electrons pass trough. So maybe this is what the sun does. It shoots fast particles that move the earth. Graviton-magnetism idea that every fast particle has a control field.

Gravity Insight guess – Maybe the Saturnus rings has something to do with gravity on Earth being a gravity dominated planet. Obviously. So gravity and magnetism belongs somehow since its a closed loop that forces the earth in its position. // PerLike magnetism a closed loop ring. Hmm. Sounds like a force is created when electrons pass trough. So maybe this is what the sun does. It shoots fast particles that move the earth.Graviton-magnetism idea that every fast particle has a control field.

Machine Learning Idea – Hyper Convolutional Layers. Train a model with ridiculus high settings on conv2D then use it as augmented label data for a bypass lower grade convolution after some middle layers. Wow HyperConvolution // Per

Machine Learning Idea – Hyper Convolutional Layers. Train a model with ridiculus high settings on conv2D then use it as augmented label data for a bypass lower grade convolution after some middle layers. Wow HyperConvolution // Per

Made a conversion model with 300 hidden size and convolution as augmented third layer label data. Wow 99.04% on MNIST with only fc layers. World record? // Per Lindholm

Machine Learning Breakthrough! – Train a large Perceptron like model with convolution bypass. Wow got 99.05% MNIST. 2000,300 size batch 100. To train large matrices use convolution then bypass it. // Per

Machine Learning Breakthrough! – Train a large Perceptron like model with convolution bypass. Wow got 99.05% MNIST. 2000,300 size batch 100. To train large matrices use convolution then bypass it. // Per

99.0% on a raspberry pi 4 2G 32GB zram (needed) MNIST

Super Intelligence Physics – Ohm’s temperature function could lead to super low resistance. Measure the resistance, voltage, current and temperature for a circuit. Assume with a u,r,i = model(…) you can lower the temperature of the circuit given variation in the variables.

Super Intelligence Physics – Ohm’s temperature function could lead to super low resistance. Measure the resistance, voltage, current and temperature for a circuit. Assume with a u,r,i = model(…) you can lower the temperature of the circuit given variation in the variables.

Salmonella vs Machine Learning Experiments – Insert an error like salmonella in the process. Classify or train on these inserted errors with a machine learning model(). Run the train model on food line without inserted errors and classify similar risks or problems. // Per

Salmonella vs Machine Learning Experiments – Insert an error like salmonella in the process. Classify or train on these inserted errors with a machine learning model(). Run the train model on food line without inserted errors and classify similar risks or problems. // Per

Revolutionary Machine Learning Physics – What if every facet matter area is a matrix function that calculates the node point movement. So to move something matter has to calculate its new position. Since it also works and is more detailed. Why not this explanation. Mega Wow //Per

Revolutionary Machine Learning Physics – What if every facet matter area is a matrix function that calculates the node point movement. So to move something matter has to calculate its new position. Since it also works and is more detailed. Why not this explanation. Mega Wow //Per

Super Intelligence Physics – Gravity – Why has the earth such a big radius. My guess is that its because the correlation coeffient somewhere between -1..1. The error outside that range is another planet interfering with earths matter. To gravity belongs stochastic variables. //Per

Machine Learning Physics – Gravity – Why has the earth such a big radius. My guess is that its because the correlation coeffient somewhere between -1..1. The error outside that range is another planet interfering with earths matter. To gravity belongs stochastic variables. //Per

Machine Learning Thinking Math – When solving a math problem with an optimizer. That is. With a machine learning model function. You can create a thinking process inbetween the sample feed. Could perhaps give insight into numerical problem solving. // Per

Machine Learning Thinking Math – When solving a math problem with an optimizer. That is. With a machine learning model function. You can create a thinking process inbetween the sample feed. Could perhaps give insight into numerical problem solving. // Per

Here thinking means have another model2() with X2 and yt2 as output for the main model(…). Like a simulation where the input and output is unknown but adapted to overal problem loss function.

Machine Learning Math guess – Imaginary unit and infinity belongs. Looking at sqrt(x) for x -> -3 I wonder if sqrt(-0.000…1) should approach negative infinity for a four part splitted number with a second_real number approaches neg infinity. To make it look better. // Per

Machine Learning Math guess – Imaginary unit and infinity belongs. Looking at sqrt(x) for x -> -3 I wonder if sqrt(-0.000…1) should approach negative infinity for a four part splitted number with a second_real number approaches neg infinity. To make it look better. // Per

Object Math Idea – Basic operation a*b = c is a classification problem solved by observing a physics simulation of an object with b m/s for a seconds or an object with a m/s for b seconds. So you need 3d space physics to solve ALL classification problems in math extended. //Per

Object Math Idea – Basic operation a*b = c is a classification problem solved by observing a physics simulation of an object with b m/s for a seconds or an object with a m/s for b seconds. So you need 3d space physics to solve ALL classification problems in math extended. //Per

At what acceleration are thoughts? If you can think in many small straight lines you can create many circles in a sphere. Normal to these circles is a virtual line. Here might some classification problem be if the object thinks fast enough. Super position of lines from many rotated circles. I assume all superposition problems belong to quantum mechanics. So gravity could be Quantum Lines ?

At what acceleration are thoughts? If you can think in many small straight lines you can create many circles in a sphere. Normal to these circles is a virtual line. Here might some classification problem be if the object thinks fast enough. Super position of lines from many rotated circles. I assume all superposition problems belong to quantum mechanics. So gravity could be Quantum Lines ?

Summation of virtual particles?

So if you did not understand it I will explain in simpler terms. Everything are classification problems so the universe can use its wisdom and expertise in solving such problems. That is. Adapt to classification problems all of it. Then gravity is also a classification problem. So you invent a problem and see what logic there is. Quantum lines are just a construct of quantum particles. A guess.

Farm innovation – Machine Learning – Insert human errors. Label the recorded sound as human affected mix with sound without human error insert. Train a machine learning model. Run the model on farmland without any inserted errors to find similar errors. // Per

Farm innovation – Machine Learning – Insert human errors. Label the recorded sound as human affected mix with sound without human error insert. Train a machine learning model. Run the model on farmland without any inserted errors to find similar errors. // Per

The idea is that the wind or noise is causality affected with problems on the farmland acre. A machine learning model can pick even the smallest event difference. Message me on per.lindholm@gmail.com

Quantum Mechanics – Pers Paradox – You probably don’t count Particles in quantum mechanics since they are so many. Then how is anything for certain? Or is it certain? – Maybe you calculate with many particles combined to make matrix functions. // Per

Quantum Mechanics – Pers Paradox – You probably don’t count in quantum mechanics since the particales are so many. Then how is anything for certain? Or is it certain? – Maybe you calculate with many particles combined to make matrix functions. // Per