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https://github.com/hasibzunair/research-notes

Stuff I find useful, mostly on AI research and computer vision.
https://github.com/hasibzunair/research-notes

computer-vision deep-learning list notes software-engineering

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Stuff I find useful, mostly on AI research and computer vision.

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README 

        
# Notes and resources useful in my projects.



## Topics

* Notes and Books

* Research

* Managing

* Technical blog posts

* Tutorials



I have listed the resources in the drop-down extensions below!



Notes and Books



* [Deep Learning Interviews: Hundreds of fully solved job interview questions from a wide range of key topics in AI](https://arxiv.org/pdf/2201.00650.pdf)

* [Introduction to Machine Learning Interviews Book, Chip Huyen](https://huyenchip.com/ml-interviews-book/)

* [Deep Learning Study Notes by Albert Pumarola](https://github.com/albertpumarola/deep-learning-notes)

* [Learn X in Y: Python](https://learnxinyminutes.com/docs/python/)

* [From Python to Numpy](https://www.labri.fr/perso/nrougier/from-python-to-numpy/)

* [Scientific Computing in Python: Introduction to NumPy and Matplotlib](https://sebastianraschka.com/blog/2020/numpy-intro.html)

* [Rules of Machine Learning: Best Practices for ML Engineering](http://martin.zinkevich.org/rules_of_ml/rules_of_ml.pdf)

* [The Good Research Code Handbook, Patrick Mineault](https://goodresearch.dev/)

* [The Little Book of Deep Learning](https://fleuret.org/public/lbdl.pdf)

* [Math for Machine Learning, Garret Thomas](https://gwthomas.github.io/docs/math4ml.pdf)

* [The Matrix Calculus You Need for Deep Learning](https://arxiv.org/abs/1802.01528)



Research

 

* [aideadlin.es](https://aideadlin.es/?sub=ML,CV)

* [You and Your Research, Richard Hamming](https://www.cs.virginia.edu/~robins/YouAndYourResearch.html)

* [Tips for Success as a New Researcher, Alex Tamkin](https://www.alextamkin.com/essays/tips-for-new-researchers)

* [Awesome Tips on various research topics by Jia-Bin Huang](https://github.com/jbhuang0604/awesome-tips)

* [Personal Rules of Productive Research, Eugene Vinitsky](https://rlblogging.notion.site/Personal-Rules-of-Productive-Research-44a456bacf7c4805a9ea417b9d3ab1b3)

* [How to do Research At the MIT AI Lab](https://dspace.mit.edu/bitstream/handle/1721.1/41487/AI_WP_316.pdf?sequence=4&isAllowed=y)

* [A Survival Guide to a PhD](http://karpathy.github.io/2016/09/07/phd/)

* [How to write the introduction, Kate Saenko](https://docs.google.com/presentation/d/1PZj0Sev2yjDu9NNr96S_wwjKCgIDhGmLjW1vtQpDhlk/edit#slide=id.p)

* [Writing a Research Statement for Graduate School and Fellowships](https://h2r.cs.brown.edu/writing-a-research-statement-for-graduate-school-and-fellowships/)

* [How to Read a CS Research Paper?](http://www2.cs.uregina.ca/~pwlfong/CS499/reading-paper.pdf)

* [How to review a paper, Nato Lambert](https://www.natolambert.com/guides/how-to-review-a-paper)

* [Building a Culture of Reproducibility in Academic Research, Jimmy Lin](https://arxiv.org/abs/2212.13534)

* [Reproducible Research Checklist](https://github.com/rdpeng/courses/blob/master/05_ReproducibleResearch/Checklist/Reproducible%20Research%20Checklist.pdf)

* [The Machine Learning Reproducibility Checklist](https://www.cs.mcgill.ca/~jpineau/ReproducibilityChecklist.pdf)

* [How to Be a Successful PhD Student](https://people.cs.umass.edu/~wallach/how_to_be_a_successful_phd_student.pdf)

* [How to be organized & productive during your PhD](https://github.com/wuningxi/Talks/blob/main/2020_How_to_be_organised_and_productive_during_your_PhD.pdf)

* [How to Read Research Papers (Andrew Ng)](https://forums.fast.ai/t/how-to-read-research-papers-andrew-ng/66892)

* [Why is the winner the best? (M. Eisenmann et al.)](https://arxiv.org/pdf/2303.17719.pdf)

* [Career Advice / Reading Research Papers, Andrew Ng](https://youtu.be/733m6qBH-jI)

* [How You Should Read Research Papers According To Andrew Ng (Stanford Deep Learning Lectures)](https://towardsdatascience.com/how-you-should-read-research-papers-according-to-andrew-ng-stanford-deep-learning-lectures-98ecbd3ccfb3)

* [How to Get Your CVPR Paper Rejected?](https://personalinterests.lipingyang.org/wp-content/uploads/2019/03/How-to-get-your-CVPR-paper-rejected.pptx.pdf)

* [How to do research, Bill Freeman, CSAIL, MIT](http://people.csail.mit.edu/billf/publications/How_To_Do_Research.pdf)

* [Research Advice, Joseph Paul Cohen, Mila](https://josephpcohen.com/w/research-advice/)

* [Lessons from my PhD, Austin Z. Henley](https://web.eecs.utk.edu/~azh/blog/lessonsfrommyphd.html)

* [How to navigate through the ML research information flood, Dmytro Mishkin](http://cmp.felk.cvut.cz/~mishkdmy/slides/How_to_navigate_no_animation.pdf)

* [An Opinionated Guide to ML Research, John Schulman](http://joschu.net/blog/opinionated-guide-ml-research.html)

* [A Year of MLC, Rosanne Liu](https://rosanneliu.com/post/a-year-of-mlc/)

* [AI research: the unreasonably narrow path and how not to be miserable](https://youtu.be/0blQp0_9NwY)

* [Research Statement, Yong Jae Lee](https://web.cs.ucdavis.edu/~yjlee/2019_researchstatement.pdf)

* [Research Statement, James Hays](http://www.cs.cmu.edu/~jhhays/info/hays_research.pdf)

* [Choose Your Weapon: Survival Strategies for Depressed AI Academics, Togelius et. al.](https://arxiv.org/pdf/2304.06035.pdf)

* [Compilation of Advice for ML PhD Students](https://yongzx.github.io/blog/posts/PhD-Advice/)

* [Build what you need and use what you build

, Micheal Black](https://medium.com/@black_51980/build-what-you-need-and-use-what-you-build-3ce559230313)



### Useful tools for research

* [Wordtune - Your thoughts in words](https://www.wordtune.com/)

* [Detexify LaTeX](http://detexify.kirelabs.org/classify.html)

* [EqnEditor](https://editor.codecogs.com/)

* [Tips for Writing a Research Paper using LaTeX](https://github.com/guanyingc/latex_paper_writing_tips)

* [Convert images of equations into LaTeX code](https://huggingface.co/spaces/hasibzunair/LaTeX-OCR-demo)

* [Top AI Tools for Business, The Neuron](https://www.theneuron.ai/top-tools?utm_source=www.theneurondaily.com&utm_medium=referral&utm_campaign=level-up-at-work)



Managing



* [1-on-1 related materials from my time as a manager, Eugene Yan](https://github.com/eugeneyan/1-on-1s) 

* [How to Become an 80/20 Manager and Achieve Exceptional Results at Work](https://betterprogramming.pub/how-to-become-an-80-20-manager-and-achieve-exceptional-results-at-work-6314fed21ac6)

* [Which analytics approaches to manage an Artificial Intelligence project? Quick guide for newbies.](https://medium.com/decathlondevelopers/which-analytics-approaches-to-manage-an-artificial-intelligence-project-quick-guide-for-newbies-1a4411a01b23)



Technical articles



### CompVis/ML/DL



* [The Early History of Computer Vision, Zbigatron](https://zbigatron.com/the-early-history-of-computer-vision/)

* [Rocket AI: 2016’s Most Notorious AI Launch and the Problem with AI Hype](https://medium.com/the-mission/rocket-ai-2016s-most-notorious-ai-launch-and-the-problem-with-ai-hype-d7908013f8c9)

* [Larry Roberts PhD Thesis, 1963](https://dspace.mit.edu/bitstream/handle/1721.1/11589/33959125-MIT.pdf?sequence=2&isAllowed=y)

* [History of computer vision contests won by deep CNNs on GPU, Jürgen Schmidhuber](https://people.idsia.ch//~juergen/computer-vision-contests-won-by-gpu-cnns.html)

* [A Revised History of Deep Learning, Jean de Dieu Nyandwi](https://www.getrevue.co/profile/deeprevision/issues/a-revised-history-of-deep-learning-issue-1-1145664)

* [How to start a deep learning project?](https://threadreaderapp.com/thread/1441027241870118913.html)

* [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://amaarora.github.io/2020/08/13/efficientnet.html?fbclid=IwAR0vxBFGVrznJ-5YXJxfjfaAuXbaHlyf61sxTpHDbllXuEvp2Tf-0x_-aO8)

* [How to Do Data Exploration for Image Segmentation and Object Detection](https://neptune.ai/blog/data-exploration-for-image-segmentation-and-object-detection)

* [Tutorial-about-3D-convolutional-network ](https://github.com/OValery16/Tutorial-about-3D-convolutional-network)

* [A Review of Different Interpretation Methods in Deep Learning](https://medium.com/@mrsalehi/a-review-of-different-interpretation-methods-in-deep-learning-part-1-saliency-map-cam-grad-cam-3a34476bc24d)

* [A Comprehensive Introduction to Different Types of Convolutions in Deep Learning](https://towardsdatascience.com/a-comprehensive-introduction-to-different-types-of-convolutions-in-deep-learning-669281e58215)

* [Deep Semi-Supervised Learning](https://yassouali.github.io/ml-blog/deep-semi-supervised/)

* [Facebook & NYU reduce Covid hospital strain — Covid Prognosis Via Self-Supervised Learning](https://towardsdatascience.com/facebook-nyu-reduce-covid-hospital-strain-covid-prognosis-via-self-supervised-learning-a30581b5e235)

* [Zero-Shot Learning](https://cetinsamet.medium.com/zero-shot-learning-53080995d45f)

* [List of sites/programs/projects that use OpenAI's CLIP neural network for steering image/video creation to match a text description](https://www.reddit.com/r/MachineLearning/comments/ldc6oc/p_list_of_sitesprogramsprojects_that_use_openais/)

* [Contrastive Representation Learning, Lilian Weng](https://lilianweng.github.io/posts/2021-05-31-contrastive/)

* [Annotated Research Paper Implementations](https://github.com/labmlai/annotated_deep_learning_paper_implementations)



### Software Engineering



* [Writing clean and optimized Python code, Youssef Hosni](https://github.com/youssefHosni/Efficient-Python-for-Data-Scientists)

* [Python best practices even data scientists should know, Yan Gobeil](https://medium.com/geekculture/python-best-practices-even-data-scientists-should-know-b86b925b8d6b)

* [Who could be your Jeff Dean?](https://medium.com/swlh/who-could-be-your-jeff-dean-6b99c25387d0)

* [The Zen of Python, Software Engineering Fundamentals, Harvard CS197](https://docs.google.com/document/d/1z5ELxpTw_U01jUB6-D6ILqHRPg6SSiLE7VFQryH3LPU/edit#)

* [Python Debugger (My Gist)](https://gist.github.com/hasibzunair/b0d7509342e5ffe4f27d1fa242613334)

* [Using Google Colab with GitHub](https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb)

* [Open a GitHub notebook in Colab](https://colab.research.google.com/github/)

* [NeurIPS 2020 ML Code Completeness Checklist](https://medium.com/paperswithcode/ml-code-completeness-checklist-e9127b168501)

* [A template README.md for code accompanying a Machine Learning paper](https://github.com/paperswithcode/releasing-research-code/blob/master/templates/README.md)

* [How Docker Can Help You Become A More Effective Data Scientist](https://towardsdatascience.com/how-docker-can-help-you-become-a-more-effective-data-scientist-7fc048ef91d5)

* [Getting started open source](https://github.com/gabrieldemarmiesse/getting_started_open_source)

* [Running a Jupyter notebook from a remote server](https://ljvmiranda921.github.io/notebook/2018/01/31/running-a-jupyter-notebook/)

* [Accessing external data from Google Colab notebooks](https://ostrokach.gitlab.io/post/google-colab-storage/)

* [How to prevent Google Colab from disconnecting?](https://medium.com/@shivamrawat_756/how-to-prevent-google-colab-from-disconnecting-717b88a128c0)

* [Documenting Python Code and Projects](https://testdriven.io/blog/documenting-python/)

* [10 Useful Jupyter Notebook Extensions for a Data Scientist](https://towardsdatascience.com/10-useful-jupyter-notebook-extensions-for-a-data-scientist-bd4cb472c25e)

* [How to improve software engineering skills as a researcher](https://ljvmiranda921.github.io/notebook/2020/11/15/data-science-swe/)

* [Bash Scripting Tutorial for Beginners](https://linuxconfig.org/bash-scripting-tutorial-for-beginners)

* [Everything gets a package? Yes, everything gets a package.](https://ericmjl.github.io/blog/2022/3/31/everything-gets-a-package-yes-everything-gets-a-package/)

* [Classifier Project Template, 

Sebastian Raschka](https://github.com/rasbt/machine-learning-notes/tree/main/templates/pl_classifier)

* [r/EngineeringResumes](https://www.reddit.com/r/EngineeringResumes/wiki/index/?rdt=33156)

* [From Microsoft Intern to Meta Staff Engineer: Raviraj Achar](https://www.developing.dev/p/from-microsoft-intern-to-meta-staff)



### AI/ML/DS in Industry



* [Building Better ML Systems — Chapter 1: Every Project Must Start with a Plan, Olga Chernytska](https://towardsdatascience.com/building-better-ml-systems-chapter-1-every-project-must-start-with-a-plan-907a36774a32)

* [What is the Team Data Science Process?](https://docs.microsoft.com/en-us/azure/architecture/data-science-process/overview)

* [How to Write Design Docs for Machine Learning Systems, Eugene Yan](https://github.com/eugeneyan/ml-design-docs)

* [What is MLOps?, Databricks](https://www.databricks.com/glossary/mlops)

* [Building efficient Experimentation Environments for ML Projects.

](https://www.newsletter.swirlai.com/p/sai-notes-05-building-efficient-experimentation?utm_source=ground-truth.beehiiv.com&utm_medium=referral&utm_campaign=computer-vision-newsletter-37)

* [Practitioners guide to MLOps: A framework for continuous delivery and automation of machine learning.](https://services.google.com/fh/files/misc/practitioners_guide_to_mlops_whitepaper.pdf)

* [Which Machine Learning Algorithm Should You Use By Problem Type?](https://medium.com/analytics-vidhya/which-machine-learning-algorithm-should-you-use-by-problem-type-a53967326566)

* [Ultimate AI Strategy Guide](https://towardsdatascience.com/ultimate-ai-strategy-guide-9bfb5e9ecf4e)

* [How I Built and Deployed a Fun Serverless Machine Learning App](https://towardsdatascience.com/building-and-deploying-cartoonify-b4786b382d7e)

* [I trained a model. What is next?](https://ternaus.blog/tutorial/2020/08/28/Trained-model-what-is-next.html)

* [10 tips to improve your machine learning models with TensorFlow](https://medium.com/decathlondevelopers/10-tips-to-improve-your-machine-learning-models-with-tensorflow-ba7c724761e2)

* [Introducing DecaVision to train image classifiers with Google’s free TPUs](https://medium.com/decathlondevelopers/introducing-decavision-to-train-image-classifiers-with-googles-free-tpus-8d216db8ad53)

* [How to become a skillful Data Scientist following the 

Decathlon

 Data Science Development Program, Alfonso Carta](https://medium.com/decathlontechnology/how-to-become-a-skillful-data-scientist-decathlon-53bb738bd58)

* [Data Science as a Product](https://blog.picnic.nl/data-science-as-a-product-f383dead5aa4)

* [Machine learning is going real-time](https://huyenchip.com/2020/12/27/real-time-machine-learning.html)

* [Data Scientists Don't Care About Kubernetes](https://determined.ai/blog/data-scientists-dont-care-about-kubernetes/)

* [Our AI ML Startups Tech Stack](https://towardsdatascience.com/our-ai-ml-startups-tech-stack-37869883d0b6)

* [Full Stack Deep Learning: Detecting deforestation from satellite images](https://towardsdatascience.com/detecting-deforestation-from-satellite-images-7aa6dfbd9f61)

* [Effective testing for machine learning systems.](https://www.jeremyjordan.me/testing-ml/)

* [Explore Computer Vision APIs, Apple Inc.](https://developer.apple.com/videos/play/wwdc2020/10673/)

* [HuggingFace Tasks: demos, use cases, models, datasets, and more](https://huggingface.co/tasks)



Tutorials



### Online Courses



* [CAP6412 Advanced Computer Vision - Spring 2023](https://www.youtube.com/playlist?app=desktop&list=PLd3hlSJsX_In7qup928HaHmilugBGctuF)

* [Harvard CS197: AI Research Experiences](https://www.cs197.seas.harvard.edu/)

* [Deep Learning for Computer Vision, Justin Johnson, UMichigan, 2020](https://youtube.com/playlist?list=PL5-TkQAfAZFbzxjBHtzdVCWE0Zbhomg7r)

* [CS231n: Convolutional Neural Networks for Visual Recognition](https://cs231n.github.io/)

* [CS229: Machine Learning, 2021](http://cs229.stanford.edu/)

* [Deep Learning Specialization](https://www.coursera.org/specializations/deep-learning)

* [Deep Learning, Francois Fleuret](https://fleuret.org/dlc/)

* [Computer Vision with Prof. Kosta Derpanis](https://www.eecs.yorku.ca/~kosta/Courses/EECS4422/)

* [Deep Learning in Computer Vision with Prof. Kosta Derpanis (York University), 2021](https://www.eecs.yorku.ca/~kosta/Courses/EECS6322/)

* [Data Preparation and Feature Engineering in ML](https://developers.google.com/machine-learning/data-prep/)

* [AIMS 2020, class on Visual Recognition by Georgia Gkioxari](https://github.com/gkioxari/aims2020_visualrecognition)

* [Reproducible Deep Learning](https://www.sscardapane.it/teaching/reproducibledl/)

* [MLOps-Basics, 2022](https://github.com/graviraja/MLOps-Basics)

* [Lightning Bits: Engineering for Researchers](https://github.com/PyTorchLightning/engineering-class)

* [Effective MLOps: Model Development](https://www.wandb.courses/courses/effective-mlops-model-development)



### Online resources



* [Image Kernels](https://setosa.io/ev/image-kernels/)

* [Convolution Visualizer](https://ezyang.github.io/convolution-visualizer/)

* [MLOps guide, Chip Huyen](https://huyenchip.com/mlops/)

* [A Complete Machine Learning Package 2021, Jean de Dieu Nyandwi](https://github.com/Nyandwi/machine_learning_complete)

* [Deep-Learning-in-Production](https://github.com/ahkarami/Deep-Learning-in-Production)

* [Machine Learning & Deep Learning Tutorials](https://github.com/ujjwalkarn/Machine-Learning-Tutorials)

* [Awesome Deep Learning](https://github.com/ChristosChristofidis/awesome-deep-learning)

* [Machine Learning cheatsheets for Stanford's CS 229](https://github.com/afshinea/stanford-cs-229-machine-learning/tree/master/en)

* [Roboflow tutorials on using SOTA computer vision models](https://github.com/roboflow/notebooks)

* [Hugging Face Transformers Tutorials](https://github.com/NielsRogge/Transformers-Tutorials)



### Ack

Inspired by https://github.com/hassony2/useful-computer-vision-phd-resources