diff --git a/contents/dl_primer/dl_primer.qmd b/contents/dl_primer/dl_primer.qmd
index 000337e6..98c8ad1d 100644
--- a/contents/dl_primer/dl_primer.qmd
+++ b/contents/dl_primer/dl_primer.qmd
@@ -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. @fig-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-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){#fig-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-dl}
 
 : Comparison of traditional machine learning and deep learning. {#tbl-mlvsdl .striped .hover}
 
diff --git a/contents/ondevice_learning/ondevice_learning.qmd b/contents/ondevice_learning/ondevice_learning.qmd
index ce104179..71ebd194 100644
--- a/contents/ondevice_learning/ondevice_learning.qmd
+++ b/contents/ondevice_learning/ondevice_learning.qmd
@@ -249,9 +249,6 @@ Transfer learning has revolutionized the way models are developed and deployed,
 
 Implementation in production scenarios can be broadly categorized into two stages: pre-deployment and post-deployment.
 
-![Training from scratch vs. transfer learning.](images/png/transfer_learning.jpeg){#transfer}
-
-
 ### Pre-Deployment Specialization
 
 In the pre-deployment stage, transfer learning acts as a catalyst to expedite the development process. Here's how it typically works: Imagine we are creating a system to recognize different breeds of dogs. Rather than starting from scratch, we can use a pre-trained model that has already mastered the broader task of recognizing animals in images.