Useful packages

• Flux.jl Machine learning API which includes built-in layers, activation and loss functions, as well as optimization routines.

• MLUtils.jl An extensible interface for handling data with data loaders, resampling, train/test splits, data partitioning, and more data handling utilities.

• Plots.jl High-level visualization interface and toolset combining multiple plotting backends into a single API. For more specific needs also check out Makie.jl. Additional course material is available here.

• Distributions.jl Everything related to probabilistic distributions. Part of the JuliaStats organization which is dedicated to providing high-quality packages for statistics.

• Optim.jl Uni- and multivariate optimization library. The Optimization.jl package provides a unified Julia API by bringing together multiple optimization packages.

• LoopVectorization.jl Provides the @turbo macro which tries to vectorize a for loop or broadcast statement. Leads to improved runtime performance if used correctly. Leads to death and destruction if not used correctly so be really careful when using it.

• Documenter.jl Combine Markdown files with inline docstrings to render a single inter-linked document. See also Reporting.

• Pluto.jl Simple browser-based Julia programming environment with built-in package manager. Add some interactivity by combining reactivity and input widgets like sliders, buttons, etc. using PlutoUI.jl. See also Reporting.

• DataFrames.jl Handle any kind of tabular data in Julia. Its design and functionality are similar to those of pandas in Python. See also Data Management.

• ProgressMeter.jl Track the status of long-running operations in Julia with a progress bar.

• JLD2.jl An efficient data format for serializing Julia objects similar to HDF5.

• Infiltrator.jl Set breakpoints in your code dropping you into an interactive REPL session.

• Debugger.jl Enter and step through functions in debugging mode.