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[PR #415] [MERGED] First 9 chapters in correct format #4600

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opened 2026-04-19 12:32:29 -05:00 by GiteaMirror · 0 comments
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📋 Pull Request Information

Original PR: https://github.com/harvard-edge/cs249r_book/pull/415
Author: @fatimajshah
Created: 8/27/2024
Status: ✅ Merged
Merged: 9/17/2024
Merged by: @profvjreddi

Base: dev ← Head: new-figures

📝 Commits (10+)

📊 Changes

16 files changed (+1599 additions, -89 deletions)

View changed files

📝 contents/ai_for_good/ai_for_good.qmd (+3 -3)
📝 contents/benchmarking/benchmarking.qmd (+2 -4)
📝 contents/data_engineering/data_engineering.qmd (+2 -2)
📝 contents/dl_primer/dl_primer.qmd (+7 -7)
📝 contents/efficient_ai/efficient_ai.qmd (+5 -6)
📝 contents/frameworks/frameworks.qmd (+10 -12)
📝 contents/hw_acceleration/hw_acceleration.qmd (+6 -6)
📝 contents/introduction/introduction.qmd (+21 -21)
📝 contents/ml_systems/ml_systems.qmd (+9 -9)
📝 contents/ondevice_learning/ondevice_learning.qmd (+4 -7)
📝 contents/ops/ops.qmd (+2 -3)
➖ contents/optimizations/images/png/kdistillation.png (+0 -0)
📝 contents/privacy_security/privacy_security.qmd (+7 -3)
📝 contents/sustainable_ai/sustainable_ai.qmd (+2 -2)
➕ contents/workflow/workflow.html (+1515 -0)
📝 contents/workflow/workflow.qmd (+4 -4)

📄 Description

Chapter 1:
Figure breakdown:

mlprojectlifecycle.png
Source: Medium
URL: https://ihsanulpro.medium.com/complete-machine-learning-project-flowchart-explained-0f55e52b9381
Placement: 1.4.7
Caption: Machine Learning Project Life Cycle

mlapplications.png
Source: Educba
URL: https://www.educba.com/applications-of-machine-learning/
Placement: 1.1
Caption: Common applications of Machine Learning

Chapter 2:
Figure breakdown

cloudml.png
Source: Original illustration
Placement: 2.2.4
Caption: Section summary - Cloud ML

edgeml.png
Source: Original illustration
Placement: 2.3.4
Caption: Section Summary - Edge ML

tinyml.png
Source: Original illustration
Placement: 2.4.4
Caption: Section Summary - Tiny ML

venndiagram.png
Source: arxiv
URL: https://arxiv.org/html/2403.19076v1
Placement: 2.5
Caption: ML Venn diagram

Chapter 3:

Figure Breakdown:

deeplearning.png
URL: https://www.leewayhertz.com/what-is-deep-learning/
Source: Leeway Hertz
Placement: Section 3.1.3
Caption: Deep learning applications, benefits and implementations.

mlvsdl.png
URL: https://aoyilmaz.medium.com/understanding-the-differences-between-deep-learning-and-machine-learning-eb41d64f1732
Source: Medium
Placement: Section 3.2.4
Caption: Comparing Machine Learning and Deep Learning.

forwardpropagation.png
URL: https://www.linkedin.com/pulse/lecture2-unveiling-theoretical-foundations-ai-machine-underdown-phd-oqsuc/
Source: Linkedin
Placement: Section 3.2
Caption: Neural networks - forward and backward propagation.

Chapter 4:
Figure Breakdown:

comparingdlandml.png
URL: https://www.bbntimes.com/technology/to-leverage-deep-learning-you-must-know-this-first
Source: BBN Times
Proposed placement: Section 4.2
Caption: Comparing Traditional Machine Learning and Deep Learning.

embeddedai.png
URL: https://www.rinf.tech/what-is-embedded-intelligence-and-how-can-tech-leaders-embrace-it/
Source: Rinf.tech
Proposed placement: Section 4.2
Caption: Embedded AI applications

Chapter 5:

datacollection.png
URL: https://www.altexsoft.com/blog/data-collection-machine-learning/
Placement: 5.4
Source: Alexsoft
Caption: Pillars of data collection.

Chapter 6:

staticvsdynamic.png
https://www.google.com/url?sa=i&url=https%3A%2F%2Fdev-jm.tistory.com%2F4&psig=AOvVaw0r1cZbZa6iImYP-fesrN7H&ust=1722533107591000&source=images&cd=vfe&opi=89978449&ved=0CBQQjhxqFwoTCLC8nYHm0YcDFQAAAAAdAAAAABAY
Caption: Comparing Static and Dynamic graphs.
Source: Dev
Placement: 6.4.2

overfittingunderfitting.png:
URL: https://www.google.com/url?sa=i&url=https%3A%2F%2Fwww.aquariumlearning.com%2Fblog-posts%2Fto-make-your-model-better-first-figure-out-whats-wrong&psig=AOvVaw3FodMJATpeLeeSsuQZBD51&ust=1722534629114000&source=images&cd=vfe&opi=89978449&ved=0CBEQjRxqGAoTCNiU49br0YcDFQAAAAAdAAAAABCoAQ
Caption: Overfitting vs. Underfitting
Source: Aquarium Learning
Placement: 6.4.7

tensorflowpytorch.png:
URL: https://www.google.com/url?sa=i&url=https%3A%2F%2Fkruschecompany.com%2Fpytorch-vs-tensorflow%2F&psig=AOvVaw1-DSFxXYprQmYH7Z4Nk6Tk&ust=1722533288351000&source=images&cd=vfe&opi=89978449&ved=0CBEQjRxqFwoTCPDhst7m0YcDFQAAAAAdAAAAABAg
Caption: PyTorch vs TensorFlow: Features and Functions
Source: K&C
Placement: 6.4.2

transferlearning.png:
URL: https://www.google.com/url?sa=i&url=https%3A%2F%2Fanalyticsindiamag.com%2Fdevelopers-corner%2Fcomplete-guide-to-understanding-precision-and-recall-curves%2F&psig=AOvVaw3MosZItazJt2eermLTArjj&ust=1722534897757000&source=images&cd=vfe&opi=89978449&ved=0CBEQjRxqFwoTCIi389bs0YcDFQAAAAAdAAAAABAw
Caption: Transfer learning.
Source: Tech Target
Placement: 6.5.4

precisionrecall.png:
URL: https://www.google.com/url?sa=i&url=https%3A%2F%2Fwww.techtarget.com%2Fsearchcio%2Fdefinition%2Ftransfer-learning&psig=AOvVaw0Cbiewbu_6NsNVf314C9Q8&ust=1722534991962000&source=images&cd=vfe&opi=89978449&ved=0CBEQjRxqFwoTCPj5jITt0YcDFQAAAAAdAAAAABAE
Caption: Reading a precision-recall curve.
Source: AIM
Placement: 6.4.7

Chapter 7: No new additions

Chapter 8:
knowledgedistillation.png:
URL:https://chukwubuikexo.medium.com/knowledge-distillation-approaches-in-machine-learning-5841a41a346a
Caption: The tutor-student framework for knowledge distillation
Source: Medium
Placement: 8.4

Chapter 9:
kdistillation.png:
URL: https://www.google.com/url?sa=i&url=https%3A%2F%2Fwww.deepset.ai%2Fblog%2Fknowledge-distillation-with-haystack&psig=AOvVaw1RZjiPK5FwkUtV1rzB_7Qv&ust=1724685648666000&source=images&cd=vfe&opi=89978449&ved=0CBQQjRxqFwoTCMDi5ey4kIgDFQAAAAAdAAAAABAE
Caption: Knowledge Distillation.
Source: Haystack
Placement: 9.2.2

🔄 This issue represents a GitHub Pull Request. It cannot be merged through Gitea due to API limitations.

## 📋 Pull Request Information **Original PR:** https://github.com/harvard-edge/cs249r_book/pull/415 **Author:** [@fatimajshah](https://github.com/fatimajshah) **Created:** 8/27/2024 **Status:** ✅ Merged **Merged:** 9/17/2024 **Merged by:** [@profvjreddi](https://github.com/profvjreddi) **Base:** `dev` ← **Head:** `new-figures` --- ### 📝 Commits (10+) - [`cf12eb0`](https://github.com/harvard-edge/cs249r_book/commit/cf12eb0875e02dbb8f8a806c0215f82741066a57) REVISED All chapters complete - [`e1a2aff`](https://github.com/harvard-edge/cs249r_book/commit/e1a2affc35a3074711db969440f4ecdae4c6873d) Merge branch 'dev' into new-figures - [`811b027`](https://github.com/harvard-edge/cs249r_book/commit/811b0275c49f42dbd74d921efd3dc9409b09be31) Fig reference fixes + embedded AI removal (should have done this seperately) - [`96892a9`](https://github.com/harvard-edge/cs249r_book/commit/96892a98bc27af54433aa943612b5079e0c8d58d) Fig ref fix - [`8c4290b`](https://github.com/harvard-edge/cs249r_book/commit/8c4290b1d119343b3ee53fd602c50c043588293f) Fig ref fix - [`96e24f4`](https://github.com/harvard-edge/cs249r_book/commit/96e24f45ea23978b91d2b97fb9c79076a64b95bd) fix figure ref - [`5589f71`](https://github.com/harvard-edge/cs249r_book/commit/5589f713d2737977d99a5a0a0229c815094c1e2b) Char fixes - [`bea1551`](https://github.com/harvard-edge/cs249r_book/commit/bea155182f30197e5168df0e227118c87dce213a) Removed unnecessary figures - [`02fe576`](https://github.com/harvard-edge/cs249r_book/commit/02fe5761f934ae767fbecd633be2dd6254cb46a2) Formatting fix + fig ref - [`a5f0011`](https://github.com/harvard-edge/cs249r_book/commit/a5f001173b0c357e12601d82ca77033d36d57d0b) fix fig references ### 📊 Changes **16 files changed** (+1599 additions, -89 deletions) <details> <summary>View changed files</summary> 📝 `contents/ai_for_good/ai_for_good.qmd` (+3 -3) 📝 `contents/benchmarking/benchmarking.qmd` (+2 -4) 📝 `contents/data_engineering/data_engineering.qmd` (+2 -2) 📝 `contents/dl_primer/dl_primer.qmd` (+7 -7) 📝 `contents/efficient_ai/efficient_ai.qmd` (+5 -6) 📝 `contents/frameworks/frameworks.qmd` (+10 -12) 📝 `contents/hw_acceleration/hw_acceleration.qmd` (+6 -6) 📝 `contents/introduction/introduction.qmd` (+21 -21) 📝 `contents/ml_systems/ml_systems.qmd` (+9 -9) 📝 `contents/ondevice_learning/ondevice_learning.qmd` (+4 -7) 📝 `contents/ops/ops.qmd` (+2 -3) ➖ `contents/optimizations/images/png/kdistillation.png` (+0 -0) 📝 `contents/privacy_security/privacy_security.qmd` (+7 -3) 📝 `contents/sustainable_ai/sustainable_ai.qmd` (+2 -2) ➕ `contents/workflow/workflow.html` (+1515 -0) 📝 `contents/workflow/workflow.qmd` (+4 -4) </details> ### 📄 Description Chapter 1: Figure breakdown: mlprojectlifecycle.png Source: Medium URL: https://ihsanulpro.medium.com/complete-machine-learning-project-flowchart-explained-0f55e52b9381 Placement: 1.4.7 Caption: Machine Learning Project Life Cycle mlapplications.png Source: Educba URL: https://www.educba.com/applications-of-machine-learning/ Placement: 1.1 Caption: Common applications of Machine Learning Chapter 2: Figure breakdown cloudml.png Source: Original illustration Placement: 2.2.4 Caption: Section summary - Cloud ML edgeml.png Source: Original illustration Placement: 2.3.4 Caption: Section Summary - Edge ML tinyml.png Source: Original illustration Placement: 2.4.4 Caption: Section Summary - Tiny ML venndiagram.png Source: arxiv URL: https://arxiv.org/html/2403.19076v1 Placement: 2.5 Caption: ML Venn diagram Chapter 3: Figure Breakdown: deeplearning.png URL: https://www.leewayhertz.com/what-is-deep-learning/ Source: Leeway Hertz Placement: Section 3.1.3 Caption: Deep learning applications, benefits and implementations. mlvsdl.png URL: https://aoyilmaz.medium.com/understanding-the-differences-between-deep-learning-and-machine-learning-eb41d64f1732 Source: Medium Placement: Section 3.2.4 Caption: Comparing Machine Learning and Deep Learning. forwardpropagation.png URL: https://www.linkedin.com/pulse/lecture2-unveiling-theoretical-foundations-ai-machine-underdown-phd-oqsuc/ Source: Linkedin Placement: Section 3.2 Caption: Neural networks - forward and backward propagation. Chapter 4: Figure Breakdown: comparingdlandml.png URL: https://www.bbntimes.com/technology/to-leverage-deep-learning-you-must-know-this-first Source: BBN Times Proposed placement: Section 4.2 Caption: Comparing Traditional Machine Learning and Deep Learning. embeddedai.png URL: https://www.rinf.tech/what-is-embedded-intelligence-and-how-can-tech-leaders-embrace-it/ Source: Rinf.tech Proposed placement: Section 4.2 Caption: Embedded AI applications Chapter 5: datacollection.png URL: https://www.altexsoft.com/blog/data-collection-machine-learning/ Placement: 5.4 Source: Alexsoft Caption: Pillars of data collection. Chapter 6: staticvsdynamic.png [https://www.google.com/url?sa=i&url=https%3A%2F%2Fdev-jm.tistory.com%2F4&psig=AOvVaw0r1cZbZa6iImYP-fesrN7H&ust=1722533107591000&source=images&cd=vfe&opi=89978449&ved=0CBQQjhxqFwoTCLC8nYHm0YcDFQAAAAAdAAAAABAY](https://dev-jm.tistory.com/4) Caption: Comparing Static and Dynamic graphs. Source: Dev Placement: 6.4.2 overfittingunderfitting.png: URL: [https://www.google.com/url?sa=i&url=https%3A%2F%2Fwww.aquariumlearning.com%2Fblog-posts%2Fto-make-your-model-better-first-figure-out-whats-wrong&psig=AOvVaw3FodMJATpeLeeSsuQZBD51&ust=1722534629114000&source=images&cd=vfe&opi=89978449&ved=0CBEQjRxqGAoTCNiU49br0YcDFQAAAAAdAAAAABCoAQ](https://www.aquariumlearning.com/blog-posts/to-make-your-model-better-first-figure-out-whats-wrong) Caption: Overfitting vs. Underfitting Source: Aquarium Learning Placement: 6.4.7 tensorflowpytorch.png: URL: [https://www.google.com/url?sa=i&url=https%3A%2F%2Fkruschecompany.com%2Fpytorch-vs-tensorflow%2F&psig=AOvVaw1-DSFxXYprQmYH7Z4Nk6Tk&ust=1722533288351000&source=images&cd=vfe&opi=89978449&ved=0CBEQjRxqFwoTCPDhst7m0YcDFQAAAAAdAAAAABAg](https://kruschecompany.com/pytorch-vs-tensorflow/) Caption: PyTorch vs TensorFlow: Features and Functions Source: K&C Placement: 6.4.2 transferlearning.png: URL: [https://www.google.com/url?sa=i&url=https%3A%2F%2Fanalyticsindiamag.com%2Fdevelopers-corner%2Fcomplete-guide-to-understanding-precision-and-recall-curves%2F&psig=AOvVaw3MosZItazJt2eermLTArjj&ust=1722534897757000&source=images&cd=vfe&opi=89978449&ved=0CBEQjRxqFwoTCIi389bs0YcDFQAAAAAdAAAAABAw](https://analyticsindiamag.com/developers-corner/complete-guide-to-understanding-precision-and-recall-curves/) Caption: Transfer learning. Source: Tech Target Placement: 6.5.4 precisionrecall.png: URL: [https://www.google.com/url?sa=i&url=https%3A%2F%2Fwww.techtarget.com%2Fsearchcio%2Fdefinition%2Ftransfer-learning&psig=AOvVaw0Cbiewbu_6NsNVf314C9Q8&ust=1722534991962000&source=images&cd=vfe&opi=89978449&ved=0CBEQjRxqFwoTCPj5jITt0YcDFQAAAAAdAAAAABAE](https://www.techtarget.com/searchcio/definition/transfer-learning) Caption: Reading a precision-recall curve. Source: AIM Placement: 6.4.7 Chapter 7: No new additions Chapter 8: knowledgedistillation.png: URL:https://chukwubuikexo.medium.com/knowledge-distillation-approaches-in-machine-learning-5841a41a346a Caption: The tutor-student framework for knowledge distillation Source: Medium Placement: 8.4 Chapter 9: kdistillation.png: URL: https://www.google.com/url?sa=i&url=https%3A%2F%2Fwww.deepset.ai%2Fblog%2Fknowledge-distillation-with-haystack&psig=AOvVaw1RZjiPK5FwkUtV1rzB_7Qv&ust=1724685648666000&source=images&cd=vfe&opi=89978449&ved=0CBQQjRxqFwoTCMDi5ey4kIgDFQAAAAAdAAAAABAE Caption: Knowledge Distillation. Source: Haystack Placement: 9.2.2 --- <sub>🔄 This issue represents a GitHub Pull Request. It cannot be merged through Gitea due to API limitations.</sub>
GiteaMirror added the pull-request label 2026-04-19 12:32:29 -05:00
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