on internet infrastructure, which allows for wicked-fast adoption of easy-to-use broad-interest services. AI Technology Compounding = Numbers Behind The Momentum22 Knowledge Distribution Evolution = Development Trending = Unprecedented37 Machine-Learning Model* Trending = In 2015... Industry Surpassed Academia as Data + Compute + Financial Needs Rose *Machine Learning = A subset of AI where machines AI, an AI Index data provider, uses the term ‘notable machine learning models’ to designate particularly influential models within the AI/machine learning ecosystem. Epoch maintains a database of 900

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121 6.6 Integration with other services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136 6.7 Utilities Python is required. Examples in the Tornado documentation will generally use the native form. Translation between the two forms is generally straightforward: # Decorated: # Native: # Normal function Locale.translate method. Templates also have the global function call _() available for string translation. The translate function has two forms: _("Translate this string") which translates the string

Queues for coroutines tornado.process — Utilities for multiple processes Integration with other services tornado.auth — Third-party login with OpenID and OAuth tornado.wsgi — Interoperability with other Python is required. Examples in the Tornado documentation will generally use the native form. Translation between the two forms is generally straightforward: # Decorated: # Native: Locale.translate method. Templates also have the global function call _() available for string translation. The translate function has two forms: _("Translate this string") which translates the string

(typemin(Float32),typemax(Float32)) (-Inf32, Inf32) julia> (typemin(Float64),typemax(Float64)) (-Inf, Inf) Machine epsilon Most real numbers cannot be represented exactly with floating-point numbers, and so for the distance between two adjacent representable floating-point numbers, which is often known as machine epsilon. Julia provides eps, which gives the distance between 1.0 and the next larger representable antic- ipate. For example, the fib(n::Integer) function above works equally well for Int arguments (machine integers) and BigInt arbitrary-precision integers (see BigFloats and BigInts), which is especially

(typemin(Float32),typemax(Float32)) (-Inf32, Inf32) julia> (typemin(Float64),typemax(Float64)) (-Inf, Inf) Machine epsilon Most real numbers cannot be represented exactly with floating-point numbers, and so for the distance between two adjacent representable floating-point numbers, which is often known as machine epsilon. Julia provides eps, which gives the distance between 1.0 and the next larger representable antic- ipate. For example, the fib(n::Integer) function above works equally well for Int arguments (machine integers) and BigInt arbitrary-precision integers (see BigFloats and BigInts), which is especially

(typemin(Float32),typemax(Float32)) (-Inf32, Inf32) julia> (typemin(Float64),typemax(Float64)) (-Inf, Inf) Machine epsilon Most real numbers cannot be represented exactly with floating-point numbers, and so for the distance between two adjacent representable floating-point numbers, which is often known as machine epsilon. Julia provides eps, which gives the distance between 1.0 and the next larger representable antic- ipate. For example, the fib(n::Integer) function above works equally well for Int arguments (machine integers) and BigInt arbitrary-precision integers (see BigFloats and BigInts), which is especially

(typemin(Float32),typemax(Float32)) (-Inf32, Inf32) julia> (typemin(Float64),typemax(Float64)) (-Inf, Inf) Machine epsilon Most real numbers cannot be represented exactly with floating-point numbers, and so for the distance between two adjacent representable floating-point numbers, which is often known as machine epsilon. Julia provides eps, which gives the distance between 1.0 and the next larger representable antic- ipate. For example, the fib(n::Integer) function above works equally well for Int arguments (machine integers) and BigInt arbitrary-precision integers (see BigFloats and BigInts), which is especially

(typemin(Float32),typemax(Float32)) (-Inf32, Inf32) julia> (typemin(Float64),typemax(Float64)) (-Inf, Inf) Machine epsilon Most real numbers cannot be represented exactly with floating-point numbers, and so for the distance between two adjacent representable floating-point numbers, which is often known as machine epsilon. Julia provides eps, which gives the distance between 1.0 and the next larger representable antic- ipate. For example, the fib(n::Integer) function above works equally well for Int arguments (machine integers) and BigInt arbitrary-precision integers (see BigFloats and BigInts), which is especially

(typemin(Float32),typemax(Float32)) (-Inf32, Inf32) julia> (typemin(Float64),typemax(Float64)) (-Inf, Inf) Machine epsilon Most real numbers cannot be represented exactly with floating-point numbers, and so for the distance between two adjacent representable floating-point numbers, which is often known as machine epsilon. Julia provides eps, which gives the distance between 1.0 and the next larger representable antic- ipate. For example, the fib(n::Integer) function above works equally well for Int arguments (machine integers) and BigInt arbitrary-precision integers (see BigFloats and BigInts), which is especially

(typemin(Float32),typemax(Float32)) (-Inf32, Inf32) julia> (typemin(Float64),typemax(Float64)) (-Inf, Inf) Machine epsilon Most real numbers cannot be represented exactly with floating-point numbers, and so for the distance between two adjacent representable floating-point numbers, which is often known as machine epsilon. Julia provides eps, which gives the distance between 1.0 and the next larger representable antic- ipate. For example, the fib(n::Integer) function above works equally well for Int arguments (machine integers) and BigInt arbitrary-precision integers (see BigFloats and BigInts), which is especially