Update fixes

contents/core/conclusion/conclusion.qmd

@@ -126,7 +126,7 @@ We anticipate a growing emphasis on data curation, labeling, and augmentation te
 
 This data-centric approach will be vital in addressing the challenges of bias, fairness, and generalizability in ML systems. By actively seeking out and incorporating diverse and inclusive datasets, we can develop more robust, equitable, and applicable models for various contexts and populations. Moreover, the emphasis on data will drive advancements in techniques such as data augmentation, where existing datasets are expanded and diversified through data synthesis, translation, and generation. These techniques can help overcome the limitations of small or imbalanced datasets, enabling the development of more accurate and generalizable models.
 
-In recent years, generative AI has taken the field by storm, demonstrating remarkable capabilities in creating realistic images, videos, and text. However, the rise of generative AI also brings new challenges for ML sysatem. Unlike traditional ML systems, generative models often demand more computational resources and pose challenges in terms of scalability and efficiency. Furthermore, evaluating and benchmarking generative models presents difficulties, as traditional metrics used for classification tasks may not be directly applicable. Developing robust evaluation frameworks for generative models is an active area of research, and something we hope to write about soon!
+In recent years, generative AI has taken the field by storm, demonstrating remarkable capabilities in creating realistic images, videos, and text. However, the rise of generative AI also brings new challenges for ML systems. Unlike traditional ML systems, generative models often demand more computational resources and pose challenges in terms of scalability and efficiency. Furthermore, evaluating and benchmarking generative models presents difficulties, as traditional metrics used for classification tasks may not be directly applicable. Developing robust evaluation frameworks for generative models is an active area of research, and something we hope to write about soon!
 
 Understanding and addressing these system challenges and ethical considerations will be important in shaping the future of generative AI and its impact on society. As ML practitioners and researchers, we are responsible for advancing the technical capabilities of generative models and developing robust systems and frameworks that can mitigate potential risks and ensure the beneficial application of this powerful technology.
 

contents/core/dnn_architectures/dnn_architectures.qmd

@@ -38,7 +38,7 @@ Deep learning architecture stands for specific representation or organizations o
 
 Neural network architectures have evolved to address specific pattern processing challenges. Whether processing arbitrary feature relationships, exploiting spatial patterns, managing temporal dependencies, or handling dynamic information flow, each architectural pattern emerged from particular computational needs. These architectures, from a computer systems perspective, require an examination of how their computational patterns map to system resources.
 
-Most often the architectures are discussed in terms of their algorithmic structures (MLPs, CNNs, RNNs, Transformers). However, in this chapter we take a more fundamental approach by examining how their computational patterns map to hardware resources. Each section analyzes how specific Pattern Processing Needss influence algorithmic structure and how these structures map to computer system resources. The implications for computer system design require examining how their computational patterns map to hardware resources. The mapping from algorithmic requirements to computer system design involves several key considerations:
+Most often the architectures are discussed in terms of their algorithmic structures (MLPs, CNNs, RNNs, Transformers). However, in this chapter we take a more fundamental approach by examining how their computational patterns map to hardware resources. Each section analyzes how specific pattern processing needs influence algorithmic structure and how these structures map to computer system resources. The implications for computer system design require examining how their computational patterns map to hardware resources. The mapping from algorithmic requirements to computer system design involves several key considerations:
 
 1. Memory access patterns: How data moves through the memory hierarchy
 2. Computation characteristics: The nature and organization of arithmetic operations