So algorithmic math and model() math meets in the singularities?
Monthly Archives: October 2018
In Climate Change Times – Would it be possible to change the education towards refugee business? Take advantage of MBA’s the right way.
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
Venezuela Innovation – Could A Farming Organization Help Venezuelan Agriculture?
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 …
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
So you got some different models() for transformations for AND, OR, …
Climate Change – Carbon Capture In The Form Of The Heavy Cabbage, Cauliflower, … ?
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
Earth History Speculation – Could A Massive Coal Asteroid Have Wiped Out The Dinosaurs With Carbon Dioxide?
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
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?
Idea – Pick Up Ocean And Beach Trash With An Industrial Robot
Possibility Existens Math – Tensile Stress Of Math Functions? As A Way To Find New Interesting Points. Other Than Center Of Mass
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.
Machine Learning Experimenting – I Wondering If Near Singular models() Are Needed To For Sharp Rises. Division Could Perhaps Do.
Blender Experiment Math – Finding Integer Solutions To a**2+b**2 = c**2. What Has The Center Of Mass To With It?
Blog Post – Halloween
Mobile Raspberry Pi – A Linux Phone Is Easy. Connect A ?Mobile Raspberry Pi With The Smartphone. Via VNC or Nomachine. Code All You Want
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
Machine Learning Math – Could There Exist Something Like Retrospective Supervised Learning. Where The model() Selects Good Succesful Calculations For A Second Good-Continous Run
Like a smart Monte Carlo method using machine learning model and some retrospective selecting
Machine Learning Math – Could Infinitesimals Be Replaced By An Adapting Machine Learning model() ?
Here adapting means iterating with model([1,0])
Machine Learning Physics – Past Fusion. Using Small Deviations From Determinism. Time Energy. Like ?Hydroelectric Power Flow Of Time // Per Lindholm 2018-oct-25
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
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
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()
