Goals: Add links that are reasonable and good explanations of how stuff works. No hype and no vendor content if possible. Practical first-hand accounts of models in prod eagerly sought.

ES2015 | |
Optimisation | |
proper tail calls (tail call optimisation) | |
Syntax | |
default function parameters | |
rest parameters | |
spread syntax for iterable objects | |
object literal extensions | |
for..of loops | |
octal and binary literals |
invoices/123
?
in a URL like /assignments?showGrades=1
.#
portion of the URL. This is not available to servers in request.url
so its client only. By default it means which part of the page the user should be scrolled to, but developers use it for various things.None of the string methods modify this
– they always return fresh strings.
charAt(pos: number): string
ES1
Returns the character at index pos
, as a string (JavaScript does not have a datatype for characters). str[i]
is equivalent to str.charAt(i)
and more concise (caveat: may not work on old engines).
#!/usr/bin/env python | |
import numpy as np | |
import numpy.linalg as la | |
def orthogonalize(U, eps=1e-15): | |
""" | |
Orthogonalizes the matrix U (d x n) using Gram-Schmidt Orthogonalization. | |
If the columns of U are linearly dependent with rank(U) = r, the last n-r columns | |
will be 0. | |
def flatten(list), do: flatten(list, []) |> Enum.reverse | |
def flatten([h | t], acc) when h == [], do: flatten(t, acc) | |
def flatten([h | t], acc) when is_list(h), do: flatten(t, flatten(h, acc)) | |
def flatten([h | t], acc), do: flatten(t, [h | acc]) | |
def flatten([], acc), do: acc |
If you are familiar with Java's generics, and are coming to Rust, you might be lead to assume that its generics are working the same way.
However, due to the different type systems, and different implementation details, there are quite a few differences between generic code in both languages.
This document tries to give a short summary about those differences:
import numpy as np | |
def gs_cofficient(v1, v2): | |
return np.dot(v2, v1) / np.dot(v1, v1) | |
def multiply(cofficient, v): | |
return map((lambda x : x * cofficient), v) | |
def proj(v1, v2): | |
return multiply(gs_cofficient(v1, v2) , v1) |