Merge branch 'dev'

contents/core/data_engineering/data_engineering.qmd

@@ -10,8 +10,6 @@ Resources: [Slides](#sec-data-engineering-resource), [Videos](#sec-data-engineer
 
 ![_DALL·E 3 Prompt: Create a rectangular illustration visualizing the concept of data engineering. Include elements such as raw data sources, data processing pipelines, storage systems, and refined datasets. Show how raw data is transformed through cleaning, processing, and storage to become valuable information that can be analyzed and used for decision-making._](images/png/cover_data_engineering.png)
 
-Let me refine this to strengthen the academic tone and remove direct reader references while maintaining the focus on data engineering principles:
-
 ## Purpose {.unnumbered}
 
 _Why does data infrastructure form the foundation of AI system success, and how does its design influence system capabilities?_

contents/core/frameworks/frameworks.qmd

@@ -10,8 +10,6 @@ Resources: [Slides](#sec-ai-frameworks-resource), [Videos](#sec-ai-frameworks-re
 
 ![_DALL·E 3 Prompt: Illustration in a rectangular format, designed for a professional textbook, where the content spans the entire width. The vibrant chart represents training and inference frameworks for ML. Icons for TensorFlow, Keras, PyTorch, ONNX, and TensorRT are spread out, filling the entire horizontal space, and aligned vertically. Each icon is accompanied by brief annotations detailing their features. The lively colors like blues, greens, and oranges highlight the icons and sections against a soft gradient background. The distinction between training and inference frameworks is accentuated through color-coded sections, with clean lines and modern typography maintaining clarity and focus._](images/png/cover_ml_frameworks.png)
 
-Let me refine this to maintain a textbook tone, remove the "By" construction, and strengthen the systems perspective:
-
 ## Purpose {.unnumbered}
 
 _How do AI frameworks bridge the gap between theoretical design and practical implementation, and what role do they play in enabling scalable machine learning systems?_

contents/core/hw_acceleration/hw_acceleration.qmd

@@ -189,13 +189,13 @@ Data locality and optimizing memory hierarchy are crucial for high throughput an
 +-----------------------------------------+-------------------------+
 | Main memory reference                   | 100 ns                  |
 +-----------------------------------------+-------------------------+
-| Compress 1K bytes with Zippy            | 3,000 ns (3 us)         | 
+| Compress 1K bytes with Zippy            | 3,000 ns (3 µs)         | 
 +-----------------------------------------+-------------------------+
-| Send 1 KB bytes over 1 Gbps network     | 10,000 ns (10 us)       | 
+| Send 1 KB bytes over 1 Gbps network     | 10,000 ns (10 µs)       | 
 +-----------------------------------------+-------------------------+
-| Read 4 KB randomly from SSD             | 150,000 ns (150 us)     |
+| Read 4 KB randomly from SSD             | 150,000 ns (150 µs)     |
 +-----------------------------------------+-------------------------+
-| Read 1 MB sequentially from memory      | 250,000 ns (250 us)     |
+| Read 1 MB sequentially from memory      | 250,000 ns (250 µs)     |
 +-----------------------------------------+-------------------------+
 | Round trip within same datacenter       | 500,000 ns (0.5 ms)     |
 +-----------------------------------------+-------------------------+
@@ -956,7 +956,7 @@ As a result, optical computing is still in the very early research stage despite
 
 Quantum computers leverage unique phenomena of quantum physics, like superposition and entanglement, to represent and process information in ways not possible classically. Instead of binary bits, the fundamental unit is the quantum bit or qubit. Unlike classical bits, which are limited to 0 or 1, qubits can exist simultaneously in a superposition of both states due to quantum effects.
 
-Multiple qubits can also be entangled, leading to exponential information density but introducing probabilistic results. Superposition enables parallel computation on all possible states, while entanglement allows nonlocal correlations between qubits. @fig-qubit visually conveys the differences between classical bits in computing and quantum bits (qbits).
+Multiple qubits can also be entangled, leading to exponential information density but introducing probabilistic results. Superposition enables parallel computation on all possible states, while entanglement allows nonlocal correlations between qubits. @fig-qubit visually conveys the differences between classical bits in computing and quantum bits (qubits).
 
 ![Qubits, the building blocks of quantum computing. Source: [Microsoft](https://azure.microsoft.com/en-gb/resources/cloud-computing-dictionary/what-is-a-qubit)](images/png/qubit.png){#fig-qubit}