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

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.

#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

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

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.

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.

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

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

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.

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.

WOW – Machine Learning NEW MLP model – Insert another question to the outputs get perfect results back. map_rnd2 = np.random.permutation(np.arange(10)) y_train2_hot = lb.transform(map_rnd2[y_train]).astype(np.float32) // Per Lindholm 98.8% on a MLP 200 element wide.

WOW – Machine Learning NEW MLP model – Insert another question to the outputs get perfect results back. map_rnd2 = np.random.permutation(np.arange(10)) y_train2_hot = lb.transform(map_rnd2[y_train]).astype(np.float32) // Per Lindholm 98.8% on a MLP 200 element wide.

Machine Learning idea – Large Paint On Models – Paint the x output from each layer with a starter model function. What would it look like 1000x larger. Then do an invers calulation on the weight matrix. And you get a much larger model. // Per

Machine Learning idea – Large Paint On Models – Paint the x output from each layer with a starter model function. What would it look like 1000x larger. Then do an invers calulation on the weight matrix. And you get a much larger model. // Per

Machine Learning – Self Hacking Math Function – y = f(t) but at time t = 0 you change y.array = y.array*0+1 . Without any rules other than that. So could this lead to something similar to self learning by optimizer. Self hacking function. The dy suddenly differs from y. // Per

Machine Learning – Self Hacking Math Function – y = f(t) but at time t = 0 you change y.array = y.array*0+1 . Without any rules other than that. So could this lead to something similar to self learning by optimizer. Self hacking function. The dy suddenly differs from y. // Per

Infinite Processing Speeds – Everything are functions – You can input different things to atom functions. Like what would happen if you made a calculating diamond. Put a CPU inside a Diamond. The CPU iterates its own max processing speed. Wow if it works. // Per

Infinite Processing Speeds – Everything are functions – You can input different things to atom functions. Like what would happen if you made a calculating diamond. Put a CPU inside a Diamond. The CPU iterates its own max processing speed. Wow if it works. // Per

Theory guess – Draw a circle many time around the nucleus by hand and you see you cant get it 100% right. The number of circles don’t overlap 100%. This is a general problem. So this loss problem gives an update assignment to the quantum particles. Leave or join the nucleus and take some energy with you. The probability is the function field from a machine learning function. Whereby the nucleus can nearfield ?teleport the electron to the right position with the quantum particles as input data. // Per

Theory guess – Draw a circle many time around the nucleus by hand and you see you cant get it 100% right. The number of circles don’t overlap 100%. This is a general problem. So this loss problem gives an update assignment to the quantum particles. Leave or join the nucleus and take some energy with you. The probability is the function field from a machine learning function. Whereby the nucleus can nearfield ?teleport the electron to the right position with the quantum particles as input data. // Per

Why don’t MIT put some research on making rain clouds. To put down wild fires. It was not difficult. Boil salt+water. Cool down. Fill a spray bottle. Spray from an elevated position. Watch rain clouds form. We need water for everything. Perfect civilization rescued. // Per

Why don’t MIT put some research on making rain clouds. To put down wild fires. It was not difficult. Boil salt+water. Cool down. Fill a spray bottle. Spray from an elevated position. Watch rain clouds form. We need water for everything. Perfect civilization rescued. // Per