Updated purpose

contents/core/dl_primer/dl_primer.qmd

@@ -10,7 +10,11 @@ Resources: [Slides](#sec-deep-learning-primer-resource), [Videos](#sec-deep-lear
 
 ![_DALL·E 3 Prompt: A rectangular illustration divided into two halves on a clean white background. The left side features a detailed and colorful depiction of a biological neural network, showing interconnected neurons with glowing synapses and dendrites. The right side displays a sleek and modern artificial neural network, represented by a grid of interconnected nodes and edges resembling a digital circuit. The transition between the two sides is distinct but harmonious, with each half clearly illustrating its respective theme: biological on the left and artificial on the right._](images/png/cover_nn_primer.png)
 
-This chapter bridges fundamental neural network concepts with real-world system implementations by exploring how different architectural patterns process information and influence system design. Instead of concentrating on algorithms or model accuracy—typical topics in deep learning algorithms courses or books—this chapter focuses on how architectural choices shape distinct computational patterns that drive system-level decisions, such as memory hierarchy, processing units, and hardware acceleration. By understanding these relationships, readers will gain the insight needed to make informed decisions about model selection, system optimization, and hardware/software co-design in the chapters that follow.
+## Purpose {.unnumbered}
+
+_How can we understand neural networks as digital representations of biological concepts, and what implications do these artificial neural networks have on system design?_
+
+This chapter is an essential primer on artificial neural networks. It focuses on translating the biological inspirations underlying these models into their computational counterparts. The goal is to bridge the gap between the intuitive principles of biological neural systems and their digital implementations. Newcomers gain foundational insights into the challenges and considerations at the intersection of biology and computation, while seasoned practitioners deepen their understanding of the systemic implications of these digital constructs. In doing so, this chapter supports the exploration of advanced deep learning architectures and methodologies in subsequent sections. It lays the groundwork for the terminology and concepts that will be central throughout the book.
 
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