Ciro Santilli @cirosantilli 37

The time-independent Schrödinger equation is a variant of the Schrödinger equation defined as:

So we see that for any Schrödinger equation, which is fully defined by the Hamiltonian $\hat{H}$, there is a corresponding time-independent Schrödinger equation, which is also uniquely defined by the same Hamiltonian.

The cool thing about the Time-independent Schrödinger equation is that we can always reduce solving the full Schrödinger equation to solving this slightly simpler time-independent version, as described at: Section "Solving the Schrodinger equation with the time-independent Schrödinger equation".

Because this method is fully general, and it simplifies the initial time-dependent problem to a time independent one, it is the approach that we will always take when solving the Schrodinger equation, see e.g. quantum harmonic oscillator.

A parameter that you choose which determines how the algorithm will perform.

In the case of machine learning in particular, it is not part of the training data set.

Hyperparameters can also be considered in domains outside of machine learning however, e.g. the step size in partial differential equation solver is entirely independent from the problem itself and could be considered a hyperparamter. One difference from machine learning however is that step size hyperparameters in numerical analysis are clearly better if smaller at a higher computational cost. In machine learning however, there is often an optimum somewhere, beyond which overfitting becomes excessive.

This was an intermediate step between the nuclear chain reaction prototype Chicago Pile-1 and the full blown plutonium mass production at Hanford site. Located in the Oak Ridge National Laboratory.

Wikipedia mentions "Since animal mtDNA evolves faster than nuclear genetic markers" with a few sources.

Some sources:

Some key points that are a bit hard to grasp, at least in some versions:

Start by looking at: Maxwell-Boltzmann vs Bose-Einstein vs Fermi-Dirac statistics.

This is a family of computers. It was a big success. It appears that this was a big unification project of previous architectures. And it also gave software portability guarantees with future systems, since writing software was starting to become as expensive as the hardware itself.

Media:

This is not bad, but some divergences to the better BBC miniseries, which presumably sticks more closely to the novel:

Related:

Quantum mechanics stuff at University of Hanover. Good teaching. Big respect:

Profiles:

The summary from www.geeksforgeeks.org/tree-traversals-inorder-preorder-and-postorder/ is a winner:

In principle one could talk about tree traversal of unordered trees as a number of possible traversals without a fixed order. But we won't consider that under this section, only deterministic ordered tree traversals.

From Wikipedia:

Topology is the plumbing of calculus.

The key concept of topology is a neighbourhood.

Just by havin the notion of neighbourhood, concepts such as limit and continuity can be defined without the need to specify a precise numerical value to the distance between two points with a metric.

As an example. consider the orthogonal group, which is also naturally a topological space. That group does not usually have a notion of distance defined for it by default. However, we can still talk about certain properties of it, e.g. that the orthogonal group is compact, and that the orthogonal group has two connected components.