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 Academia Era 2015-today: Industry Era Global Notable Machine Learning Models by Sector – 2003-2024, per Stanford HAI Annual New Notable Machine-Learning Models AI Development Trending = Unprecedented38

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 the value of the third argument. In the example above, a translation of the first string will be returned if len(people) is 1, or a translation of the second string will be returned otherwise. The most

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 the value of the third argument. In the example above, a translation of the first string will be returned if len(people) is 1, or a translation of the second string will be returned otherwise. The most

(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