Stupid TensorFlow tricks – Towards Data Science – Medium

A new take on an old (Thomson) problem using #TensorFlow

  • I wanted to see how far I could push this idea.Electrostatic charge configuration for N=625 in equilibrium.
  • Probably not.The Thomson problem is a classical physics question, “What configuration of N positive charges on the unit sphere minimizes the energy?”
  • N=11 puts the charges in a configuration that completely breaks the symmetry — while the charges are in equilibrium, they are distributed in such a way that there are more on one side than the other; it has a net dipole moment!Solving this in TF is surprisingly easy.
  • For any value of N, we can converge to a stable solution energy minima in a matter of seconds, and we can refine that to the full floating point precision in a matter of minutes by tapering down the learning rate.
  • That’s an impressive 10x speedup!Minimal energy for N=100 charges, prettified.Visualizing the configurations illustrates the regularity and the apparent symmetry, even if we are content knowing that it might not be the global minimum.

Is Google’s machine intelligence library TensorFlow (TF) good for something beyond deep learning? How well can it tackle a classic physics problem?
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Artificial Intelligence, Machine Learning and Deep Learning

Artificial Intelligence, Machine Learning and Deep Learning | #MachineLearning #Artificiali…

  • Deep learning is a subset of machine learning, which is a subset of AI.
  • Machine learning, as others have said, is a subset of AI.
  • The “learning” part of machine learning means that ML algorithms attempt to optimize along a certain dimension; i.e. they usually try to minimize error or maximize the likelihood of their predictions being true.
  • Deep learning is part of DeepMind’s notorious AlphaGo algorithm, which beat the former world champion Lee Sedol at Go in early 2016.
  • The initial guesses are quite wrong, and if you are lucky enough to have ground-truth labels pertaining to the input, you can measure how wrong your guesses are by contrasting them with the truth, and then use that error to modify your algorithm.

Read the full article, click here.


@Ronald_vanLoon: “Artificial Intelligence, Machine Learning and Deep Learning | #MachineLearning #Artificiali…”


Open-Source Deep-Learning Software for Java and Scala on Hadoop and Spark


Artificial Intelligence, Machine Learning and Deep Learning