From 28781e5494e59f0de174793d8898f6e83f9bb9cb Mon Sep 17 00:00:00 2001 From: Vijay Janapa Reddi Date: Mon, 16 Sep 2024 20:48:41 -0700 Subject: [PATCH] Fix references --- contents/dl_primer/dl_primer.qmd | 4 ++-- contents/ondevice_learning/ondevice_learning.qmd | 3 --- 2 files changed, 2 insertions(+), 5 deletions(-) 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.