# Python idea – Can you ?python code loops like an atom. Horizontal and vertical then maybe we can understand information physics. // Per

Python idea – Can you ?python code loops like an atom. Horizontal and vertical then maybe we can understand information physics. // Per

# Covid19 – Could corona virus be a Finite Machine Virus? like a flu cluster blob. Looks like an amoeba.

Covid19 – Could corona virus be a Finite Machine Virus? like a flu cluster blob. Looks like an amoeba.

# Machine Learning – Nothing is perfect – If perfect is defined as something that does not any aftertouch then wait until you meet plants. So a model function might need information for consumption under the period of its use. Split the trainingset into better groups by yt = KMeans

Machine Learning – Nothing is perfect – If perfect is defined as something that does not any aftertouch then wait until you meet plants. So a model function might need information for consumption under the period of its use. Split the trainingset X into better groups by yt(i) = KMeans

# Peace water Idea – #Fantasy #Farming – Imagine a fantasy world where fruits and veggies live in happiness (insects). Tree applemelons => !!!Tree-melonrice rice inside water melons!!! // Per

Peace water Idea – #Fantasy#Farming – Imagine a fantasy world where fruits and veggies live in happiness (insects). Tree applemelons => !!!Tree-melonrice rice inside water melons!!! // Per

# #Machine #Learning – #Quantum functions – Take an equation like x²+y²=z² => q(x,y,z)= x² + y² – z². Insert x,y,z = rand(3,100…00) to calculate all solution via histogram transfer classifcation. Probability at its fastest. // SUper Wow

#Machine#Learning#Quantum functions – Take an equation like x²+y²=z² => q(x,y,z)= x² + y² – z². Insert x,y,z = rand(3,100…00) to calculate all solution via histogram transfer classifcation. Probability at its fastest. // SUper Wow

# Machine Learning Math – An equation could give an answer that is true and false. Ex. 3 and 3.0 are integer and float. They are the same number sort of but are of different classes. // Per

Machine Learning Math – An equation could give an answer that is true and false. Ex. 3 and 3.0 are integer and float. They are the same number sort of but are of different classes. // Per

# Super Derivatives – Along came a derivative an infitesimal incriment that amplified corrected the guess.

Super Derivatives – Along came a derivative an infitesimal incriment that amplified corrected the guess.

# Win against Covid – MIT Cough like app for schools, restaurants and work. Start the app on the phone, cough, get a result with ?98.5% accuracy. Its not 100% but enough to stop a pandemic. #svpol #COVID19 #UnitedStates

Win against Covid – MIT Cough like app for schools, restaurants and work. Start the app on the phone, cough, get a result with ?98.5% accuracy. Its not 100% but enough to stop a pandemic. #svpol#COVID19#UnitedStates

# Cyber #Security – Machine Learning – Based on #histogram of random looking data to function output could you inspect the output from each layer and check the functions used. Do a md5 sum of the functions for indication of ?quantum manipulation in your machine learning model //Per

Cyber #Security – Machine Learning – Based on #histogram of random looking data to function output could you inspect the output from each layer and check the functions used. Do a md5 sum of the functions for indication of ?quantum manipulation in your machine learning model //Per

# Python Beginner – To run a filename.py file from the terminal after starting python. Write this command>>>exec(open(“filename.py”).read()) to get to the variables for easy debug. Please change google to include this exec line.

Python Beginner – To run a filename.py from the terminal after starting python.

to get to the variables for easy debug. Please change google to include this exec line.

# Machine Learning Math – Perfect sin(x) for large x? Simple use np.argmin(loss) where loss = (y-yt)**2 and yt = mean value of 100+ sin evals from 0..2pi // Per

Machine Learning Math – Perfect sin(x) for large x? Simple use np.argmin(loss) where loss = (y-yt)**2 and yt = mean value of 100+ sin evals from 0..2pi // Per

# Machine Learning Idea – Convert data points yi from a histogram plt.hist(yi,100) to a function y(x). Wow

Machine Learning Idea – Convert data points yi from a histogram plt.hist(yi,100) to a function y(x). Wow

# Machine Learning Math – guess – Functions are objects that has the same light spectrum. Guess. Similar function similar histogram from np.random.randn(10000)? Solve equations by looking at the histogram from random input? Classify each hist cluster after a function. sin(x),x**2,x

Machine Learning Math – guess – Functions are objects that has the same light spectrum. Guess. Similar function similar histogram from np.random.randn(10000)? Solve equations by looking at the histogram from random input? Classify each hist cluster after a function. sin(x),x**2,x