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Could some Rohingya work as programming instructors? Get money for food/water in some currency from facebook or youtube? It would be a value add.

November 15, 2018 Per Lindholm

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Previous PostThat is. I guess you input data from an equation into different models(). model_red(), model_green(), model_blue(),… It will be more accurate this way? This is why red is red. Problems where red is red is easier to solve that way.Next PostMachine Learning – Could a model() simulate many more layers than it can calculate due to technical problems or numerical difficulties? Also would it be possible to simulate a different kind of network with an ordinary fast network? A Simulating Kind Of Neural Network?

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By Far The Smartest Idea - Hot Milk With Cooked Rice In A Cup

If free will can be recognized in the machine learning sense then it could probably also be optimized with max as the target. // Per Lindholm

Is a proof some knowledge of the probability of the network.

The Universe Is A Compression Decompression Model() // Per Lindholm 2018

Peace Idea - Any Type Of Probability Can Move Us Forward. Peace Will Do It 100% // Per Lindholm 2018

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Machine Learning Math – The Problem Number Line the number line rewritten as valleys and peaks. The valleys representing towards zero solutions for problems. // Per Lindholm
Machine Learning Guess – Probability based models learns to take decisions based on probability. For ?certainty models there is a need for a non general infinite probability model. Continous probability values? model = function = taylor series approximation. // Per
Machine Learning 2019G Internet Idea – Measure the entropy breakdown ?pattern for light or ?radio waves. Use machine learning to classify the information associated with the transmission at the reciever end. // Per
Machine Learning Water Science Guess – What the best calculator for the job? One that flows where its suppose to. water = the moving calculator move(add(a,b)). // Per Lindholm 2019-05-23
Maybe this bird like set is a Memory Structure Variant Like a Normal Distribution?. Since the weight matrix needs to retain memory a perticular shape might help with that. // Per
Machine Learning Theory of Everything – Compression Decompression model with our compressed atom universe in middle. Noise free and under enourmous compression like venn diagram control. // Per Lindholm http://peroglyfer.se 2019-05-21
Food innovation – Try the set of possibilities. Fast Noodles + boiled water and later after 6 min add milk to cool it off and give it more flavor. // Per
Machine Learning – Hot encoding is not statistically ?friendly. I think you need the target to be a double normal distribution. Around 0 and around 1. For this you can add yt = yt +m ; m = model2(X) and then with subtract m from the model(X_test) – m.
Important Machine Learning Human Thinking – Move the body while your thinking. Move your arms, talk loud etc. Why? Since every mass unit in the body wants to improve the loss value set by the brain. There for everything adapts to find the answer. // Per
Machine Learning Guess – Could letting a layer act as incriment at the index i,j and multiply/add? with value for the next layer?
Guess – In machine learning math there is a connection between complex numbers and variation. So in F.sum(X) numbers. They to have variation in X. So there is a possibility for more variation in matrix multiplication other than F.sum() // Per
Machine Learning Battery Idea – Treat ions as if they were ML models(). Could things be added to make the ion models make better predictions. Is there battery music for better predictions within. // Per
If machine learning physics is something then antimatter is just a possibility. 0*number = 0 but what if you invent “Event numbers”. If anti number + number = anti number event. // Per
Machine Learning Physics Guess – Not a single thing can be taken for granted. Light as different frequencies ?can classify the objects position taking something like the venn diagram of freq. answer since otherwise you get an illusion objects if the are apart. // Per
Machine Learning Innovation – Since frequencies don’t change. They can then be used as universal layer loss function values. So each layer is not allowed to change the frequency. You use that as a limiter for each layer. Everything in the universe is enabling larger models // Per

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