blog update

.github/CODEOWNERS

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+*       @suneeta-mall

.github/workflows/pages.yml

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+name: Docs
+on:
+  push:
+    #branches:
+    #  - main
+permissions:
+  contents: write
+jobs:
+  deploy:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - uses: actions/setup-python@v5
+        with:
+          python-version: '3.10'
+
+      - name: install publishing dependencies
+        run: make install
+
+      - name: Deploy pages
+        run: mkdocs gh-deploy --force      

.github/workflows/renovate.yml

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+on:
+  workflow_dispatch:
+
+name: Renovate
+
+jobs:
+  check_dependencies:
+    name: Check dependencies
+    runs-on: ubuntu-22.04
+    steps:
+    - uses: actions/checkout@v4

.gitignore

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+.coverage
+.mypy_cache/
+.pip.conf
+.pytest_cache/
+.pytest_logs/
+lightning_logs/
+.venv/
+.vscode
+__pycache__
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+outputs/
+artifacts/
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+.pytest_cache/
+
+
+# Crash log files
+crash.log
+*.log
+
+# Envvars environment configuration file
+.env
+.envrc
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+.direnv
+.envrc/
+.vscode/
+.pip.conf
+.requirements-no-hashes.txt
+.python-version
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# Temporary caches
+*.so
+cache/*
+.tmp
+site
+
+.DS_Store

LICENSE.txt

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+The MIT License (MIT)

Makefile

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+.PHONY: install serve clean
+.DEFAULT_GOAL := serve
+
+install:
+	pip install -r requirements.txt
+
+serve:
+	mkdocs serve
+
+clean:
+	git clean -Xdf

README.md

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+# Random Musings
+
+Random musing of a curious engineer!
+
+
+# Setup Dev
+
+To launch mkdocs locally, follow these instructions:
+
+1. Create a Python environment:
+```bash
+python3 -m venv .venv
+. .venv/bin/activate
+```
+
+1. Install the dependencies:
+```bash
+make install
+```
+
+1. Start the serving endpoint:
+```bash
+make serve
+```
+
+# TODO
+[] Add Dep and version lock upgrade
+[] Use bib for references
+[] Add annoucement of books https://squidfunk.github.io/mkdocs-material/setup/setting-up-the-header/
+[] Format content with https://squidfunk.github.io/mkdocs-material/reference/admonitions/

docs/README.md

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+# Home 
+
+> Random musing of a curious engineer!
+
+## Book Release Announcements 
+
+I am thrilled to announce the release of "Deep Learning at Scale: At the Intersection of Hardware, Software, and Data" - an O'Reilly Book"! I have been working on this project for over 2 years with my team at O'Reilly media.
+
+### Deep Learning at Scale - An O'Reilly Book
+
+> "Deep Learning at Scale: At the Intersection of Hardware, Software, and Data" (O'Reilly) by Suneeta Mall illustrates complex concepts of full stack deep learning and reinforces them through hands-on exercises to arm you with tools and techniques to scale your project. A scaling effort is only beneficial when it's effective and efficient. To that end, this guide explains the intricate concepts and techniques that will help you scale effectively and efficiently.
+
+[![](https://a.impactradius-go.com/display-ad/15173-2121843)](https://oreillymedia.pxf.io/c/5668688/2121843/15173)
+
+
+## **Order your copy today**
+
+To order your copy, use the following links based on your preferred format:
+
+!!! note Kindle
+[:fontawesome-brands-aws: - Amazon](https://www.amazon.com/dp/B0D7F9KZWC) | [:fontawesome-brands-aws: - Amazon AU](https://www.amazon.com.au/dp/B0D7F9KZWC)
+
+!!! note Paperback
+[:fontawesome-brands-aws: - Amazon](https://www.amazon.com/dp/1098145283) | [:fontawesome-brands-aws: - Amazon AU](https://www.amazon.com.au/dp/1098145283)
+
+Alternatively, you can access the book using the 30-day trial link:
+
+!!! note "30 Days trial access by O'Reilly Media"
+[30 days trial - :fontawesome-solid-briefcase:](https://oreillymedia.pxf.io/c/5668688/2121843/15173)
+<!-- [![](https://a.impactradius-go.com/display-ad/15173-2121843)](https://oreillymedia.pxf.io/c/5668688/2121843/15173) -->
+
+## More info
+
+For more information, see details in the [project](/projects/oreilly_deep_learning_at_scale/)

docs/about.md

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+---
+title: About Me 
+---
+
+Suneeta is passionate about solving real-world problems with engineering, data, science, and machine learning. She's a PhD in applied science with a computer science and engineering background. She has extensive distributed, scalable computing and machine learning experience from IBM Software Labs, Expedita, USyd, Nearmap and more recently harrison.ai.
+
+She currently leads the AI Engineering division of [harrison.ai](https://harrison.ai/), a clinician-led artificial intelligence medical technology company tackling some of the biggest issues in healthcare causing inequitable diagnosis today.
+
+She believes in lifelong learning and is passionate about knowledge sharing. She is also an author for [O'Reilly](https://www.oreilly.com/pub/au/8214) and writes [technical blogs](https://suneeta-mall.github.io/) in her spare time.
+
+
+<br/>
+
+
+<!-- {: .oversized}
+![](/assets/img/cover.png) -->
+<br/>
+
+Education
+---------
+
+- [University of Sydney][sydu], 2019, Doctor of Philosophy. (Medical Image Optimisation and Perception/Breast Cancer/Machine Learning/Radiology)
+- [University of Sydney][sydu], 2015, Master of Applied Science (by research), Medical Image Optimisation and Perception.
+- [Harcourt Butler Technological Institute, Kanpur, India][hbti], 2007, Bachelor of Technology (BTech) Computer Science and Engineering.
+
+
+Thesis
+---------
+- [Modelling the interpretation of digital mammography using high order statistics and deep machine learning][thesis]
+
+
+Publications
+---------
+- Can a Machine Learn from Radiologists’ Visual Search Behaviour and Their Interpretation of Mammograms—a Deep-Learning Study. [Journal of Digital Imaging 2019][jdi_2019]
+- Missed cancer and visual search of mammograms: what feature-based Machine Learning can tell us that deep-convolution learning cannot. [SPIE Medical Imaging 2019][spie_2019]
+- Can digital breast tomosynthesis perform better than standard digital mammography work-up in breast cancer assessment clinic? [European Radiology 2018][eu_rad_2018]
+- A deep (learning) dive into visual search behaviour of breast radiologists. [SPIE Medical Imaging 2018][spie_2018]
+- Modeling visual search behavior of breast radiologists using a deep convolution neural network. [SPIE Journal of Medical Imaging, 2018][spie_jmi_2018]
+- Modelling the interpretation of digital mammography using high order statistics and deep machine learning. [University of Sydney, 2018][thesis]
+- Fixated and Not Fixated Regions of Mammograms  A Higher-Order Statistical Analysis of Visual Search Behavior. [Academic radiology 2017][arad_2017]
+- The role of digital breast tomosynthesis in the breast assessment clinic: a review. [Journal of Medical Radiation Science, 2017][jmrs_2017]
+- Implementation and value of using a split-plot reader design in a study of digital breast tomosynthesis in a breast cancer assessment clinic. [SPIE  Medical Imaging 2015][spie_2015]
+- Automated voice marking of a data/voice streams basing on end users profile and related data. [ip.com 2012][000214706]
+- Folksonomic approach to security systems. [ip.com 2011][000207906]
+- System and method to automatically provide optimal content based on vision and eye movement. [ip.com 2011][000208045]
+- Mechanism to conduct a whiteboard based conference session using gyroscopic enabled mobile devices. [ip.com 2011][000208037]
+- Acceptability indicators in emails. [ip.com 2011][000212197D]
+- An optimized human face detection and feature extraction algorithm. [ip.com 2010][000197147]
+
+
+Patents
+---------
+- [Display of information in computing devices][patent], 2013.
+
+
+Books
+---------
+- [Deep Learning at Scale: At the Intersection of Hardware, Software, and Data](https://www.oreilly.com/library/view/deep-learning-at/9781098145279/), 2024 by O'reilly Media
+- IBM Redbooks: Creating Plugins for Lotus Notes, Sametime, and Symphony. [IBM RedBook 2011][ibm_redbook]
+- [Curious Cassie's Beach Ride Quest](https://www.amazon.com.au/dp/B0BPQQPYD8) 2023
+- Face-Off: collection of 21 self-composed poems. [Poems, 2009][faceoff]
+
+Published courses
+---------
+- Reproducible Deep Learning is published on [O'reilly][oreilly] platform as an interactive katacoda scenario series. It is four parts course:
+    - [Reproducible Deep Learning: Semantic Segmentation on Oxford Pets Dataset]
+    - [Reproducible Deep Learning: Identifying the Reproducibility Challenge]
+    - [Reproducible Deep Learning: Random Seeds and Process-Parallelism]
+    - [Reproducible Deep Learning: Achieving 100% Reproducibility]
+
+
+Published blogs on external platforms:
+---------
+- [Suneeta@Towards Data Science]
+
+
+
+
+
+
+
+[oreilly]: https://oreilly.com
+[sydu]: https://sydney.edu.au/
+[thesis]: https://ses.library.usyd.edu.au/handle/2123/19987
+[hbti]: https://en.wikipedia.org/wiki/Harcourt_Butler_Technical_University
+[patent]: https://www.patentsencyclopedia.com/app/20130198208
+[jdi_2019]: https://link.springer.com/article/10.1007%2Fs10278-018-00174-z
+[spie_2019]: https://www.spiedigitallibrary.org/conference-proceedings-of-spie/10952/1095216/Missed-cancer-and-visual-search-of-mammograms--what-feature/10.1117/12.2512539.full
+[eu_rad_2018]: https://dx.doi.org/10.1007/s00330-018-5473-4
+[spie_2018]: https://www.spiedigitallibrary.org/conference-proceedings-of-spie/10577/1057708/A-deep-learning-dive-into-visual-search-behaviour-of-breast/10.1117/12.2293366.full
+[spie_jmi_2018]: https://www.spiedigitallibrary.org/journals/Journal-of-Medical-Imaging/volume-5/issue-3/035502/Modeling-visual-search-behavior-of-breast-radiologists-using-a-deep/10.1117/1.JMI.5.3.035502.short
+[arad_2017]: https://www.academicradiology.org/article/S1076-6332(17)30003-X/abstract
+[jmrs_2017]: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5587657/
+[spie_2015]: https://www.spiedigitallibrary.org/conference-proceedings-of-spie/9416/941619/Implementation-and-value-of-using-a-split-plot-reader-design/10.1117/12.2083152.short
+[000214706]: https://priorart.ip.com/IPCOM/000214706
+[000197147]: https://priorart.ip.com/IPCOM/000197147
+[000212197D]: https://priorart.ip.com/IPCOM/000212197D
+[000207906]: https://priorart.ip.com/IPCOM/000207906
+[000208045]: https://priorart.ip.com/IPCOM/000208045
+[000208037]: https://priorart.ip.com/IPCOM/000208037
+[ibm_redbook]: https://www-10.lotus.com/ldd/ddwiki.nsf/xpDocViewer.xsp?lookupName=IBM+Redbooks%3A+Creating+Plugins+for+Lotus+Notes%2C+Sametime%2C+and+Symphony#action=openDocument&content=catcontent&ct=redbooks
+[faceoff]: https://www.amazon.com/Face-Off-Suneeta-Mall/dp/8184650892
+[Reproducible Deep Learning: Semantic Segmentation on Oxford Pets Dataset]: https://learning.oreilly.com/scenarios/reproducible-deep-learning/9781492091219/
+[Reproducible Deep Learning: Identifying the Reproducibility Challenge]: https://learning.oreilly.com/scenarios/reproducible-deep-learning/9781492091226/
+[Reproducible Deep Learning: Random Seeds and Process-Parallelism]: https://learning.oreilly.com/scenarios/reproducible-deep-learning/9781492091233/
+[Reproducible Deep Learning: Achieving 100% Reproducibility]: https://learning.oreilly.com/scenarios/reproducible-deep-learning/9781492091240/
+[Suneeta@Towards Data Science]: https://medium.com/@suneetamall

docs/blog/.authors.yml

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+authors:
+  suneeta:
+    name: Suneeta Mall
+    description: Builder
+    avatar: https://github.com/suneeta-mall/

docs/blog/index.md

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+# Blog
+

docs/blog/posts/2019-09-10-OpenSource-ML-Datadet-links.md

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+---
+title: Links to open source ML datasets 
+categories:
+  - Machine Learning
+  - Data-science
+authors:
+  - suneeta
+date: 2019-09-10
+---
+
+# Links to open source ML datasets 
+
+- [Google Dataset Search]
+- [Wikipedia ML dataset]
+- [Pathmind]'s aggregation
+- [Computer Vision Online] aggregated source
+- 20 [Multimedia dataset] (images & videos)
+- [Hackernoon Rare dataset]
+- [Analytics vidhya]'s list of 25 sets
+
+[Google Dataset Search]: https://datasetsearch.research.google.com
+[Wikipedia ML dataset]: https://en.wikipedia.org/wiki/List_of_datasets_for_Machine Learning_research
+[Pathmind]: https://pathmind.com/wiki/open-datasets
+[Multimedia dataset]: https://lionbridge.ai/datasets/20-best-image-datasets-for-computer-vision/
+[CVonline]: http://homepages.inf.ed.ac.uk/rbf/CVonline/Imagedbase.htm
+[Hackernoon Rare dataset]: https://hackernoon.com/rare-datasets-for-computer-vision-every-Machine Learning-expert-must-work-with-2ddaf52ad862
+[Analytics vidhya]: https://www.analyticsvidhya.com/blog/2018/03/comprehensive-collection-deep-learning-datasets/