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Monthly Archives: October 2018

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Machine Learning Philosophy – Would It Be Possible To Create An Adapting model() Without The Use Of Zero In The Number System or Replace The Zero With A model(). Then Could This Avoid Singularities?

October 31, 2018 Per Lindholm

So algorithmic math and model() math meets in the singularities?

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In Climate Change Times – Would it be possible to change the education towards refugee business? Take advantage of MBA’s the right way.

October 30, 2018 Per Lindholm

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Machine Learning Physics And Chemistry Merger Idea – Just Do A data_chemistry = model(data_physics) Transform. Like Converting A Image To A Number. One Is The Chemistry Dimension The Other Is The Physics Dimension // Per Lindholm 2018-oct-30

October 30, 2018 Per Lindholm

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Venezuela Innovation – Could A Farming Organization Help Venezuelan Agriculture?

October 29, 2018 Per Lindholm

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Blender Math Education Innovation – Why Not Use Blender And Annotation pen For Making Math Videos Khan Academy Style. Use 3D Models. Zoom in Out Rotate, Split Screen With A Down scaled Version …

October 29, 2018 Per Lindholm

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South And Central America Business Innovation – Scattered Small Cheap Railway Stretches Between Cities And ?Countries. Join The Stretches Later When Business And Jobs Are Booming

October 29, 2018 Per Lindholm

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Machine Learning Physics Guess- Could Water do lossy calculations with models() of surface waves looking like graphs

October 29, 2018 Per Lindholm

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Climate Change – Carbon Capture In The Form Of The Heavy Cabbage, Cauliflower, … ?

October 29, 2018 Per Lindholm

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Psychology Innovation – A Better Approach Than Simple Conversations. Do What A Brain Does Best. Train Your Intelligence. REbuild The Brain With Python Coding Skills For Instance. Intelligence Is A Layer Of Protection That Is Acquired Not Given

October 29, 2018 Per Lindholm

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Earth History Speculation – Could A Massive Coal Asteroid Have Wiped Out The Dinosaurs With Carbon Dioxide?

October 29, 2018 Per Lindholm

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Machine Learning Physics, Forces – Since Velocities Exist. The Forward Position In The Velocity Equation Could Just Be A Prediction. Any Difference From This Prediction Is Then Met With Force From Determinism

October 28, 2018 Per Lindholm

 

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Machine Learning Innovation – Recognizing Integers Among Real Numbers. Solve More Kinds Of Problems With Labels Indicating The Number Digits Used. Adapting The model() For Decimal Trimming? As An Intermidate Step?

October 28, 2018 Per Lindholm

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Idea – Pick Up Ocean And Beach Trash With An Industrial Robot

October 28, 2018 Per Lindholm

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Possibility Existens Math – Tensile Stress Of Math Functions? As A Way To Find New Interesting Points. Other Than Center Of Mass

October 28, 2018 Per Lindholm

I was thinking about linear algebra. They had center points. As it is an interesting point. There are other points. Maybe every point on a shape and outside could have some interesting properties. So from an engineering point of view a function could have break point, stretch part and so on.

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Machine Learning Experimenting – I Wondering If Near Singular models() Are Needed To For Sharp Rises. Division Could Perhaps Do.

October 27, 2018 Per Lindholm

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Blender Experiment Math – Finding Integer Solutions To a**2+b**2 = c**2. What Has The Center Of Mass To With It?

October 27, 2018 Per Lindholm

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Blog Post – Halloween

October 27, 2018 Per Lindholm

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Mobile Raspberry Pi – A Linux Phone Is Easy. Connect A ?Mobile Raspberry Pi With The Smartphone. Via VNC or Nomachine. Code All You Want

October 27, 2018 Per Lindholm

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Machine Learning Astronomy Distance Measurement – Could The Distance To An Object Be Related To The Possible Number Of Good Data Points? For That Class Of Object // Per Lindholm 2018-oct-26

October 26, 2018 Per Lindholm

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Machine Learning Math – Could There Exist Something Like Retrospective Supervised Learning. Where The model() Selects Good Succesful Calculations For A Second Good-Continous Run

October 25, 2018 Per Lindholm

Like a smart Monte Carlo method using machine learning model and some retrospective selecting

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Machine Learning Math – Could Infinitesimals Be Replaced By An Adapting Machine Learning model() ?

October 25, 2018 Per Lindholm

Here adapting means iterating with model([1,0])

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Machine Learning Physics – Past Fusion. Using Small Deviations From Determinism. Time Energy. Like ?Hydroelectric Power Flow Of Time // Per Lindholm 2018-oct-25

October 25, 2018 Per Lindholm

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Machine Learning Physics Idea – Since Heat Goes From Warm To Cold. A Cold Matrix Gets Fully Used. How To Fill A Matrix In A Network Is Then Govern By A Differential Heat Equation? For Filling Big Matrix Problems. Where The Update Algorithm Otherwise Would Be Lost. If It Tried To Change Everything At Once

October 25, 2018 Per Lindholm

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Machine Learning Idea – Run The model() Against Virtual Problems. There Should Exist A Motion Type Similar To Brownian Motion For Every Problem. Continuous Updates To The Weight Matrices Similar To Living – The Start Of A Problem Solution As Supervised Learning

October 25, 2018 Per Lindholm

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Machine Learning Innovation Idea – Run The model() Against Imaginary And Real problems. Like Time Degradation. The Information That Survives Will Be Important To The model()

October 24, 2018 Per Lindholm

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Ideas Are Free To Use As Long As You Give Me Credit

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

:wow

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Recent Posts

  • Machine Learning Guess – As a possibility you can wait for some of equations in the system. Why solve all at the same time. Could produce more complex paths otherwise. Here I waited for y1-y2 -> 0
  • Machine Learning Innovation Idea – Generalisation by same language model0(). y1 = model1(model0(X1)), y2 = model2(model0(X2)),y3 = model3(model0(X3)),… Where X1 = MNIST sample and X2 = Cifar10 sample And X3 = something else in image format. Image showing most linear recognition //Per
  • Towards a Machine Learning Super Battery – Are different laser printed hole patterns different in their ion properties like capacity and transport? Laser aided CD printed like battery// Per
  • Machine Learning Innovation – Air Condition Device – If There is a need of a compressor. I wonder if you can alter the surface function with a cylinder. Or better yet use a peltier element connected to the front end of the piston. So it cools the hot gas. //Per
  • Blender 3D Innovation Idea – What if you take three render models() and iterate until you have enough intersecting pixel color values. Could this set be the inital value basis of a differential equation that solves the overall image with super high resolution? Venn Diagram Usage
  • Towards A Super Battery – Inspired by ?saltpaper. Could sandpaper or paper with crystals be a no brainer thing of a super battery?
  • Machine Learning Physics Idea – Predicting simulated turbulence with a w = model(rnge()).
  • Machine Learning Math Idea – choice_division(a,b). Where you get a rest that moves between the even parts. Dynamic object?
  • Machine Learning Physics – My most important tweet ever. Small fusion so that we have time and money to get the data samples. Million+?
  • Machine Learning Complex Math – Doing some complex root finding with machine learning model() and chainer framework. y**2 = 1+1j gives y = 1.0986842+0.45508987j wow
  • Machine Learning Physics – Force Or Acceleration Which Comes First. ( ? ) * F = m * a * ( ? ). My answer is that both are something else at the very start and that ( ? ) iterates fast to 1. // Per Lindholm 2019
  • Ultra Intelligent Way To Learn – First Read a little Bit Then Innovate By Guessing a Next Little Step. Read the next little Bit and guessinnovate the next. Keep doing so and you will be able to innovate forever.
  • Machine Learning 0..1 Math – Finding new functions? Math Universe needs to Split the first largest areas of Sum(every function the x = 0..1 window). Indication of a new function. Fill in the holes in the 0..1 window and you get something like a fractal. Conjectory – All functions applied to the window x = 0..1 makes up a fractal.
  • Machine Learning Insight Idea – Internally model() Initiated For Loops? For when there is no iteration-path and you have to use a little brute force.
  • Machine Learning Insight – Super Duper Calculations – y , sane = Classification(Regression(X)) , sane(y)

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