fix fig references

contents/dl_primer/dl_primer.qmd

@@ -54,11 +54,11 @@ Organizations worldwide recognize deep learning's transformative potential and i
 
 ### Applications of Deep Learning
 
-Deep learning is extensively used across numerous industries today, and its transformative impact on society is evident. In finance, it powers stock market prediction, risk assessment, and fraud detection. For instance, deep learning algorithms can predict stock market trends, guide investment strategies, and improve financial decisions. In marketing, it drives customer segmentation, personalization, and content optimization. Deep learning analyzes consumer behavior and preferences to enable highly targeted advertising and personalized content delivery. In manufacturing, deep learning streamlines production processes and enhances quality control by continuously analyzing large volumes of data. This allows companies to boost productivity and minimize waste, leading to the production of higher quality goods at lower costs. In healthcare, machine learning aids in diagnosis, treatment planning, and patient monitoring. Similarly, deep learning can make medical predictions that improve patient diagnosis and save lives. The benefits are clear: machine learning predicts with greater accuracy than humans and does so much more quickly.@deeplearning further illustrates some applications of deep learning.
+Deep learning is extensively used across numerous industries today, and its transformative impact on society is evident. In finance, it powers stock market prediction, risk assessment, and fraud detection. For instance, deep learning algorithms can predict stock market trends, guide investment strategies, and improve financial decisions. In marketing, it drives customer segmentation, personalization, and content optimization. Deep learning analyzes consumer behavior and preferences to enable highly targeted advertising and personalized content delivery. In manufacturing, deep learning streamlines production processes and enhances quality control by continuously analyzing large volumes of data. This allows companies to boost productivity and minimize waste, leading to the production of higher quality goods at lower costs. In healthcare, machine learning aids in diagnosis, treatment planning, and patient monitoring. Similarly, deep learning can make medical predictions that improve patient diagnosis and save lives. The benefits are clear: machine learning predicts with greater accuracy than humans and does so much more quickly. @fig-deeplearning further illustrates some applications of deep learning.
 
 Deep learning enhances everyday products, such as strengthening Netflix's recommender systems to provide users with more [personalized recommendations](https://dl.acm.org/doi/abs/10.1145/3543873.3587675). At Google, deep learning models have driven significant improvements in [Google Translate](https://research.google/blog/recent-advances-in-google-translate/), enabling it to handle over [100 languages](https://cloud.google.com/translate/docs/languages). Autonomous vehicles from companies like Waymo, Cruise, and Motional have become a reality through the use of deep learning in their [perception system](https://motional.com/news/technically-speaking-improving-av-perception-through-transformative-machine-learning). Additionally, Amazon employs deep learning at the edge in their Alexa devices to perform [keyword spotting](https://towardsdatascience.com/how-amazon-alexa-works-your-guide-to-natural-language-processing-ai-7506004709d3).
 
-![Deep learning applications, benefits and implementations. Source: [Leeway Hertz](https://www.leewayhertz.com/what-is-deep-learning/)](images/png/deeplearning.png){#deeplearning}
+![Deep learning applications, benefits and implementations. Source: [Leeway Hertz](https://www.leewayhertz.com/what-is-deep-learning/)](images/png/deeplearning.png){#fig-deeplearning}
 
 ### Relevance to Embedded AI
 
@@ -126,9 +126,9 @@ The forward pass is the initial phase where data moves through the network from
 
 :::{#vid-nn .callout-important}
 
-@forward-propagation explains the concept of forward pass using an illustration.
+@fig-forward-propagation explains the concept of forward pass using an illustration.
 
-![Neural networks - forward and backward propagation. Source: [Linkedin](https://www.linkedin.com/pulse/lecture2-unveiling-theoretical-foundations-ai-machine-underdown-phd-oqsuc/)](images/png/forwardpropagation.png){#forward-propagation}
+![Neural networks - forward and backward propagation. Source: [Linkedin](https://www.linkedin.com/pulse/lecture2-unveiling-theoretical-foundations-ai-machine-underdown-phd-oqsuc/)](images/png/forwardpropagation.png){#fig-forward-propagation}
 
 # Neural Networks
 
@@ -233,7 +233,7 @@ These architectures serve specific purposes and excel in different domains, offe
 
 ### Traditional ML vs Deep Learning
 
-Deep learning extends traditional machine learning by utilizing neural networks to discern patterns in data. In contrast, traditional machine learning relies on a set of established algorithms such as decision trees, k-nearest neighbors, and support vector machines, but does not involve neural networks. To briefly highlight the differences, @tbl-mlvsdl illustrates the contrasting characteristics between traditional ML and deep learning. @ml-vs-dl further explains the differences between Machine Learning and Deep Learning.
+Deep learning extends traditional machine learning by utilizing neural networks to discern patterns in data. In contrast, traditional machine learning relies on a set of established algorithms such as decision trees, k-nearest neighbors, and support vector machines, but does not involve neural networks. To briefly highlight the differences, @tbl-mlvsdl illustrates the contrasting characteristics between traditional ML and deep learning. @fig-ml-vs-dl further explains the differences between Machine Learning and Deep Learning.
 
 +-------------------------------+-----------------------------------------------------------+--------------------------------------------------------------+
 | Aspect                        | Traditional ML                                            | Deep Learning                                                |
@@ -251,7 +251,7 @@ Deep learning extends traditional machine learning by utilizing neural networks
 | Maintenance                   | Easier (simple to update and maintain)                    | Complex (requires more efforts in maintenance and updates)   |
 +-------------------------------+-----------------------------------------------------------+--------------------------------------------------------------+ 
 
-![Comparing Machine Learning and Deep Learning. Source: [Medium](https://aoyilmaz.medium.com/understanding-the-differences-between-deep-learning-and-machine-learning-eb41d64f1732)](images/png/mlvsdl.png){#ml-vs-dl}
+![Comparing Machine Learning and Deep Learning. Source: [Medium](https://aoyilmaz.medium.com/understanding-the-differences-between-deep-learning-and-machine-learning-eb41d64f1732)](images/png/mlvsdl.png){#fig-ml-vs-dl}
 
 : Comparison of traditional machine learning and deep learning. {#tbl-mlvsdl .striped .hover}
 

contents/efficient_ai/efficient_ai.qmd

@@ -76,22 +76,20 @@ Model compression methods are essential for bringing deep learning models to dev
 
 ![Different forms of quantization.](images/jpg/quantization.jpeg){#fig-quantization}
 
-**Knowledge Distillation:** Knowledge distillation involves training a smaller model (student) to replicate the behavior of a larger model (teacher). The idea is to transfer the knowledge from the cumbersome model to the lightweight one. Hence, the smaller model attains performance close to its larger counterpart but with significantly fewer parameters. @knowledge-dist demonstrates the tutor-student framework for knowledge distillation. We will explore knowledge distillation in more detail in the @sec-kd.
+**Knowledge Distillation:** Knowledge distillation involves training a smaller model (student) to replicate the behavior of a larger model (teacher). The idea is to transfer the knowledge from the cumbersome model to the lightweight one. Hence, the smaller model attains performance close to its larger counterpart but with significantly fewer parameters. @fig-knowledge-dist demonstrates the tutor-student framework for knowledge distillation. We will explore knowledge distillation in more detail in the @sec-kd.
 
-![The tutor-student framework for knowledge distillation. Source: [Medium](URL:https://chukwubuikexo.medium.com/knowledge-distillation-approaches-in-machine-learning-5841a41a346a)](images/png/knowledgedistillation.png){#knowledge-dist}
+![The tutor-student framework for knowledge distillation. Source: [Medium](URL:https://chukwubuikexo.medium.com/knowledge-distillation-approaches-in-machine-learning-5841a41a346a)](images/png/knowledgedistillation.png){#fig-knowledge-dist}
 
 
 ## Efficient Inference Hardware
 
-In the [Training](../training/training.qmd) chapter, we discussed the process of training AI models. Now, from an efficiency standpoint, it’s important to note that training is a resource and time-intensive task, often requiring powerful hardware and taking anywhere from hours to weeks to complete. Inference, on the other hand, needs to be as fast as possible, especially in real-time applications. This is where efficient inference hardware comes into play. By optimizing the hardware specifically for inference tasks, we can achieve rapid response times and power-efficient operation, which is especially crucial for edge devices and embedded systems.
+In the [Training](../training/training.qmd) chapter, we discussed the process of training AI models. Now, from an efficiency standpoint, it's important to note that training is a resource and time-intensive task, often requiring powerful hardware and taking anywhere from hours to weeks to complete. Inference, on the other hand, needs to be as fast as possible, especially in real-time applications. This is where efficient inference hardware comes into play. By optimizing the hardware specifically for inference tasks, we can achieve rapid response times and power-efficient operation, which is especially crucial for edge devices and embedded systems.
 
 **TPUs (Tensor Processing Units):** [TPUs](https://cloud.google.com/tpu) are custom-built ASICs (Application-Specific Integrated Circuits) by Google to accelerate machine learning workloads [@jouppi2017datacenter]. They are optimized for tensor operations, offering high throughput for low-precision arithmetic, and are designed specifically for neural network machine learning. TPUs significantly accelerate model training and inference compared to general-purpose GPU/CPUs. This boost means faster model training and real-time or near-real-time inference capabilities, crucial for applications like voice search and augmented reality.
 
 [Edge TPUs](https://cloud.google.com/edge-tpu) are a smaller, power-efficient version of Google's TPUs tailored for edge devices. They provide fast on-device ML inferencing for TensorFlow Lite models. Edge TPUs allow for low-latency, high-efficiency inference on edge devices like smartphones, IoT devices, and embedded systems. AI capabilities can be deployed in real-time applications without communicating with a central server, thus saving bandwidth and reducing latency. Consider the table in @fig-edge-tpu-perf. It shows the performance differences between running different models on CPUs versus a Coral USB accelerator. The Coral USB accelerator is an accessory by Google's Coral AI platform that lets developers connect Edge TPUs to Linux computers. Running inference on the Edge TPUs was 70 to 100 times faster than on CPUs.
 
-![Accelerator vs CPU performance comparison across different hardware configurations. Desktop CPU: 64-bit Intel(R) Xeon(R) E5–1650 v4 @ 3.60GHz. Embedded CPU: Quad-core Cortex-A53 @ 1.5GHz, †Dev Board: Quad-core Cortex-A53 @ 1.5GHz + Edge TPU. Source: [TensorFlow Blog.](https://blog.tensorflow.org/2019/03/build-ai-that-works-offline-with-coral.html)](images/png/tflite_edge_tpu_perf.png){#fig-edge-tpu-perf}
-
-
+![Accelerator vs CPU performance comparison across different hardware configurations. Desktop CPU: 64-bit Intel(R) Xeon(R) E5-1650 v4 @ 3.60GHz. Embedded CPU: Quad-core Cortex-A53 @ 1.5GHz, †Dev Board: Quad-core Cortex-A53 @ 1.5GHz + Edge TPU. Source: [TensorFlow Blog.](https://blog.tensorflow.org/2019/03/build-ai-that-works-offline-with-coral.html)](images/png/tflite_edge_tpu_perf.png){#fig-edge-tpu-perf}
 
 **NN (Neural Network) Accelerators:** Fixed-function neural network accelerators are hardware accelerators designed explicitly for neural network computations. They can be standalone chips or part of a larger system-on-chip (SoC) solution. By optimizing the hardware for the specific operations that neural networks require, such as matrix multiplications and convolutions, NN accelerators can achieve faster inference times and lower power consumption than general-purpose CPUs and GPUs. They are especially beneficial in TinyML devices with power or thermal constraints, such as smartwatches, micro-drones, or robotics.