To effectively build deep learning models with Julia, a strong grasp of its features suited for machine learning is beneficial. This chapter revisits key aspects of the language and its environment, preparing you for the subsequent topics on model implementation.

In this chapter, you will cover:

• Why Julia is well-suited for computationally demanding tasks typical in machine learning.
• How Julia's type system and multiple dispatch contribute to writing flexible and performant ML code.
• Important Julia packages for data science operations, such as DataFrames.jl and CSV.jl.
• Numerical computation techniques using arrays and linear algebra, which are fundamental to deep learning algorithms.
• The basic mechanism of automatic differentiation, a core component of training neural networks.
• An overview of the available tools and libraries for machine learning within Julia.
• Guidance on setting up your Julia development environment for deep learning.
• Practical exercises on data manipulation and basic algorithm implementation using Julia.