1602774000
This lecture is dedicated for variations of gradient descent algorithms. We talk about Stochastic & mini-batch Gradient Descent along with Simulated Annealing. Python implementations are done on Jupyter.
#machine learning
1605809940
This lecture is dedicated for variations of gradient descent algorithms. We talk about Stochastic & mini-batch Gradient Descent along with Simulated Annealing. Python implementations are done on Jupyter.
⏲Outline⏲
00:00 Introduction
00:26 Simulated Annealing
02:42 Stochastic Gradient Descent with variable step size
10:12 Mini-batch Gradient Descent
Subscribe: https://www.youtube.com/channel/UCgC1d4JZ1Fz4t8MWLJD464w
#machine-learning #tensorflow #scikit-learn
1665369120
Cellular Automata
A cellular automaton is a collection of "colored" cells on a grid of specified shape that evolves through a number of discrete time steps according to a set of rules based on the states of neighboring cells. The rules are then applied iteratively for as many time steps as desired.
mathworld.wolfram.com/CellularAutomaton
To generate an elementary cellular automaton, use
ca = CellularAutomaton(rule, init, gen)
where rule
is the Wolfram code (integer), init
is a vector containing the initial starting condition and gen
is the number of generations to be computed. For a single starting cell in the middle just omit the init
vector.
To generate 15 generations of elementary cellular automaton of rule 90 use
using CellularAutomata
ca90 = CellularAutomaton(90, 16)
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For a more complex cellular automaton you can change the number of states k
the cell can be and the radius r
of neighbors that can influence the states. If k
is changed to be larger than 2, a totalistic CA is computed where only the average value of all neighbors count. This can be done like this
ca = CellularAutomaton(993, 15, k=3)
X
XXX
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X X
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X # X
XXX ### XXX
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X # X # X
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XXX X XX # # XX X XXX
X# #X XX###X## ##X###XX X# #X
Two dimensional cellular automaton (like Conway's Game of Life) can be created by
ca = CA2d(B, S, init, gen)
where B
and S
are vectors that have the numbers of neighboring cells that define when cell is born or survives, init
(matrix) is the initial starting condition and gen
is the number of generations the CA is to be computed.
Game of life is then run for 9 generations for e.g. a turbine pattern by typing
ca = CA2d([3], [2, 3], init, 9)
1st step
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To run tests, execute the following command from the root folder of the repository:
julia tests/run_tests.jl
Author: Natj
Source Code: https://github.com/natj/CellularAutomata.jl
License: MIT license
1620898103
Check out the 5 latest technologies of machine learning trends to boost business growth in 2021 by considering the best version of digital development tools. It is the right time to accelerate user experience by bringing advancement in their lifestyle.
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