With the fundamentals of creating and training neural networks in Julia and Flux.jl covered, this chapter addresses more advanced techniques and operational aspects. We will focus on methods to significantly speed up your model training and execution, particularly through the use of Graphics Processing Units (GPUs).

You will learn to:

• Configure and use CUDA.jl to run Flux.jl models on NVIDIA GPUs, managing data effectively between the CPU and GPU.
• Profile your models to identify performance bottlenecks and apply optimization strategies.
• Work with pre-existing models, a common practice for building upon established architectures.
• Understand basic generative modeling approaches with Flux.jl.
• Become aware of other deep learning libraries available in the Julia ecosystem and understand how to integrate Python libraries into your Julia workflows.
• Finally, we will discuss pathways for preparing your Julia deep learning applications for deployment.

By completing this chapter, you'll be better equipped to handle more demanding deep learning tasks, optimize your models for speed, and consider the practicalities of using your models in broader applications.