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.