With the groundwork of Julia for machine learning covered, we now turn to Flux.jl. This library is the primary tool in Julia for developing neural networks. Its design prioritizes flexibility and utilizes Julia's features for efficient computation, making it a strong choice for deep learning projects.
This chapter will guide you through the essentials of Flux.jl. You will learn to construct models using its core elements: layers, chains, and activation functions. We will examine how to define loss functions to measure model error and how optimizers direct the learning process. The role of Zygote.jl in providing automatic differentiation for Flux will also be explained. The chapter culminates in building and applying a basic neural network, giving you practical experience with these concepts.
2.1 Flux.jl: Design Principles and Architecture
2.2 Flux.jl Primitives: Layers, Models, and Chains
2.3 Defining Simple Neural Network Layers
2.4 Working with Activation Functions in Flux
2.5 Loss Functions: Measuring Model Error
2.6 Optimizers: Guiding the Learning Process
2.7 Zygote.jl: Automatic Differentiation in Flux
2.8 Constructing a Basic Neural Network in Flux
2.9 Hands-on Practical: A Simple Regressor with Flux
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