This package is intended as a lightweight foundation for tensor operations across the Julia ecosystem. Currently it exports three operations: hadamard, tensor, and boxdot.

This package provides an implementation of an associative container mapping (K,V) pairs via the type IntervalTreeK, V. The type K may be any ordered type.

DeepDiffs.jl provides the deepdiff function, which finds and displays differences (diffs) between Julia data structures. It supports Vectors, Dicts, and Strings. When diffing dictionaries where values associated with a particular key may change, deepdiff will recurse into value to provide a more detailed diff.

This package contains a common suite of test functions for stressing the robustness of differentiation tools.

Jive.jl is a Julia package to help with writing tests.

Measurements.jl is an error propagation calculator and library for physical measurements. It supports real and complex numbers with uncertainty, arbitrary precision calculations, operations with arrays, and numerical integration. The linear error propagation theory is employed to propagate the errors.

This package defines functionality to calculate volume element changes for functions that perform a change of variables (like coordinate transformations).

This is a Julia package that defines an IniFile type that interfaces with .ini files.

When visualizing images, it is not uncommon to provide a 2D view of different image sources. For example, comparing multiple images of different sizes, getting a preview of machine learning dataset. This package aims to provide easy-to-use tools for such tasks.

This package provides several functions to manipulate strings with ANSI escape sequences.

This package provides alignment algorithms and data structures for sequence of DNA, RNA, and amino acid sequences.

This library provides tools for working with Julia code and expressions. This includes a template-matching system and code-walking tools that let you do deep transformations of code.

This package provides the IOCapture.capture(f) function, which captures the standard output and standard error, and returns it as a string together with the return value.

This package defines the BFloat16 data type. The only currently available hardware implementation of this datatype are Google's Cloud TPUs. As such, this package is suitable to evaluate whether using TPUs would cause precision problems for any particular algorithm, even without access to TPU hardware. Note that this package is designed for functionality, not performance, so this package should be used for precision experiments only, not performance experiments.

Optimisers.jl defines many standard gradient-based optimisation rules, and tools for applying them to deeply nested models.

This package provides various examples.

This package provides a minimal String type for Julia that allows for efficient string representation and transfer

This package provides string distances in Julia. Distances are defined for AbstractStrings, and any iterator that define length(). The package also defines Distance "modifiers" that can be applied to any distance.

This package provides a simple and flexible IR format, expressive enough to work with both lowered and typed Julia code, as well as external IRs. It can be used with Julia metaprogramming tools such as Cassette.

This package contains generic helper algorithms for building plotting components.

This package provides a macro-based implementation of traits. The main idea behind traits is to group types outside the type-hierarchy and to make dispatch work with that grouping. The difference to Union-types is that types can be added to a trait after the creation of the trait, whereas Union types are fixed after creation.

ImageInTerminal.jl is a drop-in package that once imported changes a how a single Colorant and whole Colorant arrays (i.e. Images) are displayed in the interactive REPL. The displayed images will be downscaled to fit into the size of your active terminal session.

PositiveFactorizations is a package for computing a positive definite matrix decomposition (factorization) from an arbitrary symmetric input. The motivating application is optimization (Newton or quasi-Newton methods), in which the canonical search direction -H/g (H being the Hessian and g the gradient) may not be a descent direction if H is not positive definite.

This is a small package to make it easier to type LaTeX equations in string literals in the Julia language.