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https://github.com/imteekay/machine-learning-research

✨ ML/AI Research
https://github.com/imteekay/machine-learning-research
data-science deep-learning machine-learning python science
Last synced: 10 days ago
JSON representation
✨ ML/AI Research
Host: GitHub
URL: https://github.com/imteekay/machine-learning-research
Owner: imteekay
License: mit
Created: 2017-12-30T21:32:23.000Z (almost 7 years ago)
Default Branch: master
Last Pushed: 2024-10-28T10:51:28.000Z (13 days ago)
Last Synced: 2024-10-28T12:43:14.114Z (13 days ago)
Topics: data-science, deep-learning, machine-learning, python, science
Language: Jupyter Notebook
Homepage:
Size: 32.3 MB
Stars: 72
Watchers: 6
Forks: 17
Open Issues: 0
Metadata Files:
- Readme: README.md
- Funding: FUNDING.yml
- License: LICENSE
Awesome Lists containing this project

awesome-resources-cs - Machine Learning
README

        

# ML Research

## Table of Contents

- [ML Research](#ml-research)

  - [Table of Contents](#table-of-contents)

  - [Mathematics](#mathematics)

    - [General Math](#general-math)

    - [How to learn mathematics](#how-to-learn-mathematics)

    - [Linear Algebra](#linear-algebra)

    - [Statistics](#statistics)

    - [Calculus](#calculus)

    - [Optimization](#optimization)

  - [Artificial Intelligence](#artificial-intelligence)

    - [Learning Roadmap](#learning-roadmap)

    - [Data Science Fundamentals](#data-science-fundamentals)

    - [Machine Learning](#machine-learning)

    - [Support Vector Machines](#support-vector-machines)

    - [Advanced Machine Learning](#advanced-machine-learning)

    - [Deep Learning](#deep-learning)

    - [Generative AI](#generative-ai)

    - [Books](#books)

    - [Podcasts](#podcasts)

    - [Communities](#communities)

    - [Online Courses](#online-courses)

    - [Questions and Answers](#questions-and-answers)

    - [Data Science Journey](#data-science-journey)

    - [ML/AI \& Healthcare](#mlai--healthcare)

    - [ML/AI \& Biology](#mlai--biology)

    - [Databases](#databases)

    - [Lists](#lists)

  - [Science](#science)

    - [Fundamentals](#fundamentals)

    - [Science](#science-1)

    - [Cancer](#cancer)

    - [Genetics](#genetics)

    - [Computational Biology](#computational-biology)

    - [Precision Health](#precision-health)

    - [Meta](#meta)

    - [Central Resources](#central-resources)

    - [Science: Q\&A](#science-qa)

  - [Projects](#projects)

  - [Careers](#careers)

  - [People](#people)

  - [Research \& Laboratories](#research--laboratories)

  - [License](#license)

## Mathematics

### General Math

- [Data Science Math Skills](https://www.coursera.org/learn/datasciencemathskills)

- [Mathematics of Big Data and Machine Learning](https://ocw.mit.edu/courses/res-ll-005-mathematics-of-big-data-and-machine-learning-january-iap-2020)

- [Mathematics for Machine Learning](https://github.com/imteekay/mathematics-for-machine-learning)

- [How to get from high school math to cutting-edge ML/AI](https://www.justinmath.com/how-to-get-from-high-school-math-to-cutting-edge-ml-ai)

### How to learn mathematics

- [How to study math — Jo Boaler](https://www.youtube.com/watch?v=pRsutB2NhLk&list=TLPQMjkwNzIwMjND3tvET8TH0g&index=2&ab_channel=LexClips)

- [How To Self-Study Math](https://www.youtube.com/watch?v=fb_v5Bc8PSk&list=TLPQMjkwNzIwMjND3tvET8TH0g&index=3&ab_channel=TheMathSorcerer)

- [How to learn physics & math](https://www.youtube.com/watch?v=klEFaIZuiYk&list=TLPQMjkwNzIwMjND3tvET8TH0g&index=4&ab_channel=Tibees)

- [Best Way to Learn Math](https://www.youtube.com/watch?v=zvrleanEYOw&list=TLPQMjkwNzIwMjND3tvET8TH0g&index=5&ab_channel=LexClips)

- [How to learn math — Jordan Ellenberg](https://www.youtube.com/watch?v=UcpmwBOVp44&list=TLPQMjkwNzIwMjND3tvET8TH0g&index=6&ab_channel=LexClips)

- [Learn Mathematics from START to FINISH](https://www.youtube.com/watch?v=pTnEG_WGd2Q&t=17s&ab_channel=TheMathSorcerer)

- [How to Learn Math](https://math.ucr.edu/home/baez/books.html#math)

- [Why Learn Discrete Math?](https://www.youtube.com/watch?v=oJhAPsy9hBU&ab_channel=Intermation)

### Linear Algebra

- [Linear Algebra at MIT](https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/video-lectures)

- [Khan Academy Linear Algebra](https://www.khanacademy.org/math/linear-algebra)

- [Linear algebra cheat sheet for deep learning](https://towardsdatascience.com/linear-algebra-cheat-sheet-for-deep-learning-cd67aba4526c)

- [[Course] Essence of linear algebra](https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab)

- [[Course] Linear Algebra Crash Course](https://www.youtube.com/watch?v=n9jZmymHX6o&ab_channel=LunarTech)

- [Linear Algebra Tutorial](https://www.youtube.com/watch?v=3Bf9oh7nkus&ab_channel=metacodeM)

- [Tiled Matrix Multiplication](https://penny-xu.github.io/blog/tiled-matrix-multiplication)

- [The Big Picture of Linear Algebra](https://www.youtube.com/watch?v=ggWYkes-n6E)

- [[Interview] Gilbert Strang: Linear Algebra](https://www.youtube.com/watch?v=lEZPfmGCEk0)

- [Mathematics for Machine Learning - Linear Algebra](https://www.youtube.com/playlist?list=PLiiljHvN6z1_o1ztXTKWPrShrMrBLo5P3)

- [Linear Algebra for Data Science](https://drive.google.com/file/d/1nJVwdQV9zp-Q9VQenZF0-HOOG6L2lEOD/view)

### Statistics

- [Khan Academy Probability](https://www.khanacademy.org/math/probability)

- [Khan Academy Statistics and probability](https://www.khanacademy.org/math/statistics-probability)

- [Inferential Statistics](https://br.udacity.com/course/intro-to-inferential-statistics--ud201)

- [Introduction to Statistics](https://www.coursera.org/learn/stanford-statistics)

- [The better way to do statistics](https://www.youtube.com/watch?v=3jP4H0kjtng)

- [A complete guide to box plots](https://www.atlassian.com/data/charts/box-plot-complete-guide)

- [Probability and Statistics](https://www.youtube.com/playlist?list=PLMrJAkhIeNNR3sNYvfgiKgcStwuPSts9V)

### Calculus

- [[Course] Essence of calculus](https://www.youtube.com/playlist?list=PLZHQObOWTQDMsr9K-rj53DwVRMYO3t5Yr)

- [Khan Academy Multivariable Calculus](https://www.khanacademy.org/math/multivariable-calculus)

- [Khan Academy Differential Calculus](https://www.khanacademy.org/math/differential-calculus)

- [Calculus Applied](https://www.edx.org/learn/calculus/harvard-university-calculus-applied)

- [Mathematics for Machine Learning - Multivariate Calculus](https://www.youtube.com/playlist?list=PLiiljHvN6z193BBzS0Ln8NnqQmzimTW23)

### Optimization

- [Convex Optimization](https://web.stanford.edu/class/ee364a/videos.html)

## Artificial Intelligence

### Learning Roadmap

- [Machine Learning Roadmap 2022](https://www.youtube.com/watch?v=y4o9hrSCDPI&list=TLPQMzAxMjIwMjMIRqKttLLFsg&index=3&ab_channel=SmithaKolan-MachineLearningEngineer)

- [How to learn AI and ML](https://www.youtube.com/watch?v=KEB-w9DUdCw&ab_channel=PythonProgrammer)

- [Recommendations by Ilya Sutskever](https://arc.net/folder/D0472A20-9C20-4D3F-B145-D2865C0A9FEE)

### Data Science Fundamentals

- [Fundamental Python Data Science Libraries: Numpy](https://hackernoon.com/fundamental-python-data-science-libraries-a-cheatsheet-part-1-4-58884e95c2bd)

- [Fundamental Python Data Science Libraries: Pandas](https://hackernoon.com/fundamental-python-data-science-libraries-a-cheatsheet-part-2-4-fcf5fab9cdf1)

- [Fundamental Python Data Science Libraries: Matplotlib](https://hackernoon.com/fundamental-python-data-science-libraries-a-cheatsheet-part-3-4-6c2aecc697a4)

- [Fundamental Python Data Science Libraries: Scikit-Learn](https://hackernoon.com/fundamental-python-data-science-libraries-a-cheatsheet-part-4-4-fd8895ef85d5)

- [Data Engineering Roadmap](https://github.com/hasbrain/data-engineer-roadmap)

### Machine Learning

- [Intro to Machine Learning](https://www.kaggle.com/learn/intro-to-machine-learning)

- [Intermediate Machine Learning](https://www.kaggle.com/learn/intermediate-machine-learning)

- [Introduction to Machine Learning Course](https://www.udacity.com/course/intro-to-machine-learning--ud120)

- [Learning Math for Machine Learning](https://blog.ycombinator.com/learning-math-for-machine-learning)

- [Machine Learning at CMU](http://www.cs.cmu.edu/~tom/10701_sp11/lectures.shtml)

- [Bishop Keynotes on ML](https://www.microsoft.com/en-us/research/people/cmbishop/#!videos)

- [Machine Learning Guides by Google](https://developers.google.com/machine-learning/guides)

- [Machine Learning Crash Course by Google](https://developers.google.com/machine-learning/crash-course/ml-intro)

- [Facebook Field Guide to Machine Learning](https://research.fb.com/the-facebook-field-guide-to-machine-learning-video-series)

- [Um pequeno guia para Data Science / Machine Learning](http://lgmoneda.github.io/2017/06/12/data-science-guide.html)

- [Machine Learning for All](https://www.coursera.org/learn/uol-machine-learning-for-all)

- [Reinforcement Learning](https://www.udacity.com/course/reinforcement-learning--ud600)

- [Machine Learning Crash Course with TensorFlow APIs](https://developers.google.com/machine-learning/crash-course)

- [Backpropagation from the ground up](https://www.youtube.com/watch?v=SmZmBKc7Lrs)

- [Understanding Machine Learning: From Theory to Algorithms](https://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf)

- [CS229 Lecture Notes](https://cs229.stanford.edu/lectures-spring2022/main_notes.pdf)

- [A theory-heavy intro to machine learning](https://0xpemulis.net/learningtheory.html)

- [ML Code Challenges](https://www.deep-ml.com)

- [Machine learning in Python with scikit-learn](https://lms.fun-mooc.fr/courses/course-v1:inria+41026+session03/6c7bd3e1d86545c4b723b844ae2702f9)

- [Introduction to Algorithms and Machine Learning](https://www.justinmath.com/files/introduction-to-algorithms-and-machine-learning.pdf)

- [How to actually learn AI/ML: Reading Research Papers](https://www.youtube.com/watch?v=x6slke5niqw)

- [Machine Learning Fundamentals: Bias and Variance](https://www.youtube.com/watch?v=EuBBz3bI-aA)

- [Machine Learning Fundamentals: Cross Validation](https://www.youtube.com/watch?v=fSytzGwwBVw)

- [Machine Learning Specialization by Andrew Ng](https://www.youtube.com/playlist?list=PLkDaE6sCZn6FNC6YRfRQc_FbeQrF8BwGI)

- [AI Fundamentals](https://www.udacity.com/course/ai-fundamentals--ud099)

- [Artificial Intelligence](https://www.udacity.com/course/artificial-intelligence--ud954)

- [📃 Hyper-Parameter Optimization: A Review of Algorithms and Applications](https://arxiv.org/pdf/2108.02497)

- [📃 How to avoid machine learning pitfalls: a guide for academic researchers](https://arxiv.org/pdf/2108.02497)

- [📃 Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning](https://arxiv.org/pdf/1811.12808)

- [Árvore de decisão](https://www.youtube.com/watch?v=W7MfsE5av0c)

- [📃 How to avoid machine learning pitfalls: a guide for academic researchers](https://arxiv.org/pdf/2108.02497)

### Support Vector Machines

- [Support Vector Machines Part 1 (of 3): Main Ideas](https://www.youtube.com/watch?v=efR1C6CvhmE)

- [Support Vector Machines Part 2: The Polynomial Kernel](https://www.youtube.com/watch?v=Toet3EiSFcM)

- [Support Vector Machines Part 3: The Radial (RBF) Kernel](https://www.youtube.com/watch?v=Qc5IyLW_hns)

- [MIT — Learning: Support Vector Machines](https://www.youtube.com/watch?v=_PwhiWxHK8o)

- [Support Vector Machines | Stanford CS229](https://www.youtube.com/watch?v=lDwow4aOrtg)

### Advanced Machine Learning

- [Interpretable Machine Learning](https://christophm.github.io/interpretable-ml-book)

### Deep Learning

- [Intro to Deep Learning](https://www.kaggle.com/learn/intro-to-deep-learning)

- [Deep Learning Book](http://www.deeplearningbook.org)

- [fast.ai vs. deeplearning.ai](https://medium.com/@markryan_69718/learning-deep-learning-fast-ai-vs-deeplearning-ai-34f9c42cf701)

- [Deep Learning with Python](https://www.manning.com/books/deep-learning-with-python)

- [Dive into Deep Learning](https://d2l.ai/index.html)

- [Intro to Deep Learning](http://introtodeeplearning.com/2020/index.html)

- [Intro to Deep Learning with PyTorch](https://www.udacity.com/course/deep-learning-pytorch--ud188)

- [Deep Learning Research and the Future of AI](https://www.youtube.com/watch?v=5BrNt38OraE&ab_channel=MicrosoftResearch)

- [[Paper] Sequence to Sequence Learning with Neural Networks](https://arxiv.org/pdf/1409.3215)

- [Demystifying deep reinforcement learning](https://nail.cs.ut.ee/index.php/2015/12/19/globular-star-cluster-radio-scope-great-turbulent-clouds)

- [A Review of: Human-Level Control through deep Reinforcement Learning](https://hci.iwr.uni-heidelberg.de/system/files/private/downloads/213797145/report_carsten_lueth_human_level_control.pdf)

- [[Paper] Mastering the game of Go without human knowledge](https://www.nature.com/articles/nature24270.epdf?author_access_token=VJXbVjaSHxFoctQQ4p2k4tRgN0jAjWel9jnR3ZoTv0PVW4gB86EEpGqTRDtpIz-2rmo8-KG06gqVobU5NSCFeHILHcVFUeMsbvwS-lxjqQGg98faovwjxeTUgZAUMnRQ)

- [AlphaGo Zero: Starting from scratch](https://deepmind.google/discover/blog/alphago-zero-starting-from-scratch)

- [Neural Networks](https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi)

- [The Principles of Deep Learning Theory](https://arxiv.org/pdf/2106.10165)

- [Why do tree-based models still outperform deep learning on tabular data?](https://arxiv.org/pdf/2207.08815)

- [MIT 6.S191: Introduction to Deep Learning](https://www.youtube.com/playlist?list=PLtBw6njQRU-rwp5__7C0oIVt26ZgjG9NI)

- [Deep Learning NYU](https://www.youtube.com/playlist?list=PLLHTzKZzVU9e6xUfG10TkTWApKSZCzuBI)

- [Building Neural Networks from Scratch](https://www.youtube.com/playlist?list=PLPTV0NXA_ZSj6tNyn_UadmUeU3Q3oR-hu)

- [The Matrix Calculus You Need For Deep Learning](https://explained.ai/matrix-calculus)

  - [Paper](https://arxiv.org/pdf/1802.01528)

- [Convolution is Matrix Multiplication](https://penny-xu.github.io/blog/convolution-is-matrixmultiplication)

- [Neural Networks and Deep Learning — Course 1](https://www.youtube.com/watch?v=CS4cs9xVecg&list=PLkDaE6sCZn6Ec-XTbcX1uRg2_u4xOEky0)

- [Improving Deep Neural Networks — Course 2](https://www.youtube.com/playlist?list=PLkDaE6sCZn6Hn0vK8co82zjQtt3T2Nkqc)

- [Structuring Machine Learning Projects — Course 3](https://www.youtube.com/playlist?list=PLkDaE6sCZn6E7jZ9sN_xHwSHOdjUxUW_b)

- [Convolutional Neural Networks — Course 4](https://www.youtube.com/playlist?list=PLkDaE6sCZn6Gl29AoE31iwdVwSG-KnDzF)

- [Sequence Models — Course 5](https://www.youtube.com/playlist?list=PLkDaE6sCZn6F6wUI9tvS_Gw1vaFAx6rd6)

- [Understanding Deep Learning Book Club](https://www.youtube.com/playlist?list=PLmp4AHm0u1g0AdLp-LPo5lCCf-3ZW_rNq)

- [Dive into Deep Learning](https://d2l.ai)

- [TABPFN: A transformer that solves small tabular classification problems in a second](https://arxiv.org/pdf/2207.01848)

- [A Matemática das Redes Neurais](https://www.youtube.com/watch?v=qZ9xuPcoWSA)

- [Introdução a Redes Neurais e Deep Learning](https://www.youtube.com/watch?v=Z2SGE3_2Grg)

- [How do neural networks learn features from data?](https://www.youtube.com/watch?v=y0KxsLJvG14)

### Generative AI

- [Intuitions on Language Models & Shaping the Future of AI from the History of Transformer](https://www.youtube.com/watch?v=3gb-ZkVRemQ&ab_channel=StanfordOnline)

### Books

- [The Elements of Statistical Learning](https://web.stanford.edu/~hastie/Papers/ESLII.pdf)

- [Pattern Recognition and Machine Learning](http://users.isr.ist.utl.pt/~wurmd/Livros/school/Bishop%20-%20Pattern%20Recognition%20And%20Machine%20Learning%20-%20Springer%20%202006.pdf)

- [Python Machine Learning](https://www.amazon.com/Python-Machine-Learning-scikit-learn-TensorFlow/dp/1787125939)

- [Python Data Science Handbook](https://github.com/jakevdp/PythonDataScienceHandbook)

- [Think Stats: Exploratory Data Analysis in Python](http://greenteapress.com/thinkstats2/html/index.html)

- [The Orange Book of Machine Learning](https://carl-mcbride-ellis.github.io/TOBoML/TOBoML.pdf)

### Podcasts

- [Data Science, Past, Present and Future with Hilary Mason](https://www.datacamp.com/community/podcast/data-science-past-present-and-future)

### Communities

- [Machine Learning Reddit](https://www.reddit.com/r/MachineLearning)

- [NLP Reddit](https://www.reddit.com/r/LanguageTechnology)

- [Statistics Reddit](https://www.reddit.com/r/statistics)

- [Data Science Reddit](https://www.reddit.com/r/datascience)

- [Machine Learning Quora Topic](https://www.quora.com/topic/Machine-Learning)

- [Statistics Quora Topic](https://www.quora.com/topic/Statistics-academic-discipline)

- [Data Science Quora Topic](https://www.quora.com/topic/Data-Science)

- [Lee Lab of AI for bioMedical Sciences](https://suinlee.cs.washington.edu)

- [Lab of big data and predictive analysis in healthcare](https://www.fsp.usp.br/labdaps)

- [Jean Fan lab](https://jef.works)

- [Pranav Rajpurkar](https://pranavrajpurkar.com)

- [The AI Health Podcast](https://twitter.com/AIHealthPodcast)

### Online Courses

- [Preprocessing for Machine Learning in Python](https://www.datacamp.com/courses/preprocessing-for-machine-learning-in-python)

- [Computer Science for Artificial Intelligence](https://www.edx.org/professional-certificate/harvardx-computer-science-for-artifical-intelligence)

- [Machine Learning courses](https://www.edx.org/learn/machine-learning)

- [Machine Learning Crash Course with TensorFlow APIs](https://developers.google.com/machine-learning/crash-course)

- [Machine Learning Stanford Course](https://www.coursera.org/learn/machine-learning)

- [Machine Learning with Python](https://www.coursera.org/learn/machine-learning-with-python)

- [Math for Machine Learning with Python](https://www.edx.org/learn/math/edx-math-for-machine-learning-with-python)

- [Machine Learning with Python: from Linear Models to Deep Learning](https://www.edx.org/learn/machine-learning/massachusetts-institute-of-technology-machine-learning-with-python-from-linear-models-to-deep-learning)

### Questions and Answers

- [Andrew Ng's answer on "How should you start a career in Machine Learning?"](https://www.quora.com/How-should-you-start-a-career-in-Machine-Learning)

- [How do I learn mathematics for machine learning?](https://www.quora.com/How-do-I-learn-mathematics-for-machine-learning)

- [How do I learn machine learning?](https://www.quora.com/How-do-I-learn-machine-learning-1)

### Data Science Journey

- [How to land a Data Scientist job at your dream company — My journey to Airbnb](https://towardsdatascience.com/how-to-land-a-data-scientist-job-at-your-dream-company-my-journey-to-airbnb-f6a1e99892e8)

- [How to build a data science project from scratch](https://medium.freecodecamp.org/how-to-build-a-data-science-project-from-scratch-dc4f096a62a1)

### ML/AI & Healthcare

- [[Course] Machine Learning for Healthcare](https://www.edx.org/learn/machine-learning/massachusetts-institute-of-technology-machine-learning-for-healthcaregit)

- [AI in Healthcare @ Google Brain](https://www.youtube.com/watch?v=cvXVK8oqU4Q&ab_channel=AlexanderAmini)

- [Healthcare's AI Future: A Conversation with Fei-Fei Li & Andrew Ng](https://www.youtube.com/watch?v=Gbnep6RJinQ&ab_channel=StanfordHAI)

- [AI and the Future of Health](https://www.microsoft.com/en-us/research/blog/ai-and-the-future-of-health)

- [Aplicações de Deep Learning a Genética](https://www.youtube.com/watch?v=GiL6RnXLjvI)

- [Daphne Koller: Biomedicine and Machine Learning](https://www.youtube.com/watch?v=xlMTWfkQqbY&ab_channel=LexFridman)

- [Data and resource needs for machine learning in genomics](https://www.youtube.com/watch?v=kjQ-8LFkeaA&ab_channel=NationalHumanGenomeResearchInstitute)

- [Machine Learning para Predições em Saúde](https://www.youtube.com/playlist?list=PLpvV74h3lihLdYrlnhlx_phy4pFZeZsKx)

- [Inteligência Artificial em Saúde](https://www.youtube.com/playlist?list=PLAudUnJeNg4tvUFZ8tXQDoAkFAASQzOHm)

- [[Course] Collaborative Data Science for Healthcare](https://www.edx.org/learn/data-science/massachusetts-institute-of-technology-collaborative-data-science-for-healthcare)

- [[Course] Data Analytics and Visualization in Health Care](https://www.edx.org/learn/data-analysis/rochester-institute-of-technology-data-analytics-and-visualization-in-health-care)

- [[Course] Introduction to Applied Biostatistics: Statistics for Medical Research](https://www.edx.org/learn/biostatistics/osaka-university-introduction-to-applied-biostatistics-statistics-for-medical-research)

- [[Paper] Capabilities of Gemini Models in Medicine](https://arxiv.org/pdf/2404.18416)

  - [Journal Club Debate: Capacidades dos modelos Gemini na medicina](https://www.youtube.com/watch?v=qj-4_dP6BQw)

- [[Paper] Deep learning methods for drug response prediction in cancer: Predominant and emerging trends](https://www.frontiersin.org/articles/10.3389/fmed.2023.1086097/full)

- [[Paper] Machine Learning Prediction of Cancer Cell Sensitivity to Drugs Based on Genomic and Chemical Properties](https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0061318&type=printable)

- [[Paper] Artificial intelligence in healthcare: past, present and future](https://svn.bmj.com/content/svnbmj/2/4/230.full.pdf)

- [Multimodal Generative AI: the Next Frontier in Precision Health](https://www.microsoft.com/en-us/research/quarterly-brief/mar-2024-brief/articles/multimodal-generative-ai-the-next-frontier-in-precision-health)

- [Artificial Intelligence in Healthcare: Past, Present and Future](https://svn.bmj.com/content/svnbmj/2/4/230.full.pdf)

- [[Paper] The myth of generalisability in clinical research and machine learning in health care](https://www.thelancet.com/action/showPdf?pii=S2589-7500%2820%2930186-2)

- [[Paper] Sybil: A Validated Deep Learning Model to Predict Future Lung Cancer Risk From a Single Low-Dose Chest Computed Tomography](https://ascopubs.org/doi/pdfdirect/10.1200/JCO.22.01345)

- [[Paper] Capabilities of Gemini Models in Medicine](https://arxiv.org/abs/2404.18416)

- [[Paper] Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine](https://arxiv.org/pdf/2311.16452)

- [Large Language Models Encode Clinical Knowledge](https://arxiv.org/pdf/2212.13138)

- [AI Aspirations Healthcare Futures](https://www.youtube.com/watch?v=Bn5M6hT3W1E)

- [Breast Cancer Prediction: project](https://github.com/imteekay/breast-cancer-prediction)

- [Training ML Models for Cancer Tumor Classification](https://www.iamtk.co/training-ml-models-for-cancer-tumor-classification)

- [AI for Business Transformation: Lessons from Healthcare](https://www.youtube.com/watch?v=8C-XiXB67_Q)

- [The revolution in high-throughput proteomics and AI](https://www.science.org/doi/10.1126/science.ads5749)

- [[Course] AI for Medicine Specialization](https://www.deeplearning.ai/courses/ai-for-medicine-specialization)

- [Towards Democratization of Subspeciality Medical Expertise](https://arxiv.org/pdf/2410.03741)

- [Uncovering early predictors of cerebral palsy through the application of machine learning: a case-control study](https://bmjpaedsopen.bmj.com/content/bmjpo/8/1/e002800.full.pdf)

- [Development and Validation of a Deep Learning Method to Predict Cerebral Palsy From Spontaneous Movements in Infants at High Risk](https://watermark.silverchair.com/groos_2022_oi_220608_1656698661.11703.pdf?token=AQECAHi208BE49Ooan9kkhW_Ercy7Dm3ZL_9Cf3qfKAc485ysgAAAzEwggMtBgkqhkiG9w0BBwagggMeMIIDGgIBADCCAxMGCSqGSIb3DQEHATAeBglghkgBZQMEAS4wEQQM1jHKezBOqHeyGBSVAgEQgIIC5IKGlXjEvmbfHnrMFH3WqDX3nEMySJWaqxi9RQk-_fsW1yrXRseVGAYSDEElc6gPIbMpTmJ4hHCYzQhvIQ4igHIgJCq6U_8git_LEJR2GLAS3VE8HjBUsH0pqmhWDJ24P6WW94jgn9ZL3nPTrZX2nU6uL_ZFtDAo64muDnJ54N0NPgrxUQJOCyKz65neeKqVVM8mO6F60HFzbBuPapBMKVlTVpyB3UfDtVw7VuCPdFNyiQ8A7Z7EmzWR7rXL93pg2KztZ2-0qWqN8upA0XgN4N01LOFsanLZIf6TaZ5TTjpaAuWtgSrwurxJW5Wh3A2a6zr8SrfpGq92muV3XHJ4CtElyitZ1z9BKjZDkSrQSv9jpG8Of0ngOna4xCDwIMJ6CmaV9cajxs9ARCzmUlWyNxiVenwXCLR1z-x_W9QEeuTT58BUB9fRVStPKngy-7IG4IWbOaxAP8sLa50CtUkBPtOnichM0pdJWkuDYvOv_ylqDzoGjT6VVPk_wLVjJPGlisp9V0ZLea7gDI5OuHOfcDTO6rjWwynkUNAHZYM_dHCkBG0rFSlqxKarpOUMRR0Z6RqPJiAFzYGBnTBs2kpI0Ax5UD1Dhk2wxRcu7z8UALf-riLDXIzJZDXp_o8dHZW2HL5809Kt6k5OFiV5ovUenCXBLCDBhZC1I9r6bQD9M-CvDCBFP2vVNNUzIlT2ARGgluxXP_BOp3dQFSy3V5dwWR2vHhqj8_WFjn7kLPAiqNtjotcwYYPXrMg7mROH--dC3fwzZ608O5KXiZo717_1ftjNrWfQ-SYpq2nkkxIAln4NmoGsuIZgqHaTwZvmacMt-q0y6TQRSRkKVRhIWtF0XjVcjzlqfmOOwmF8ehdsmMnovU1pL_vDGqj2TSVMpkgG3oQ6dHR-6OAzAlvsNtV_QsFxiWUCof9MSXafhQGGWaCoyTvXwK2Iy7lEwYYzu1H0WvpywrJD1SNqa4gcgo-KHZOInjVj)

### ML/AI & Biology

- [[Paper] Machine learning-aided generative molecular design](https://www.nature.com/articles/s42256-024-00843-5)

- [Causal Inference for Computational Biology](https://summit.sfu.ca/_flysystem/fedora/2023-05/etd22427.pdf)

- [Simulating 500 million years of evolution with a language model](https://evolutionaryscale-public.s3.us-east-2.amazonaws.com/research/esm3.pdf)

- [Learning to Plan Chemical Syntheses](https://www.semanticscholar.org/reader/ef8ab2a0be51a0cd04c2c0f01adfae956a2a84af)

- [Machine Learning for Genomics](https://www.youtube.com/playlist?list=PLypiXJdtIca6dEYlNoZJwBaz__CdsaoKJ)

- [MIT Deep Learning in Life Sciences](https://www.youtube.com/playlist?list=PLypiXJdtIca5sxV7aE3-PS9fYX3vUdIOX)

- [AI Text2Protein Breakthrough Tackles the Molecule Programming Challenge](https://medium.com/310-ai/mpm4-ai-text2protein-breakthrough-tackles-the-molecule-programming-challenge-870045a8c1ad)

- [Genomic Language Models: Opportunities and Challenges](https://arxiv.org/pdf/2407.11435)

- [Melodia: A Python Library for Protein Structure Analysis](https://watermark.silverchair.com/btae468.pdf?token=AQECAHi208BE49Ooan9kkhW_Ercy7Dm3ZL_9Cf3qfKAc485ysgAAA4swggOHBgkqhkiG9w0BBwagggN4MIIDdAIBADCCA20GCSqGSIb3DQEHATAeBglghkgBZQMEAS4wEQQMVNfSCiowdD1a6WnjAgEQgIIDPsK_bI3A6IGF7cjZqL-1PehaqGZsY0AwhsIWIAc5Qa0rKYxHgeqnDIClLsf0Ey_I6ps6u545OlMuxBXd7yIO3xB0N0EMbsq5qYVSHqnuiqu2-LShlZxwk0ICGlLuJDR0ROgvGT837Lh72d2Eax_WuXzx6bkr9L2eUBifW8x4fULkCBqFtvhySkJwwvIIYd46Pi8bgM-XeQZI1DjwxN4KuHG15xkQpbdGvMmYpSGJvJefQTnY_YzF94F7zheUVj4s3JRYpKPtbxhG-6ba525xHNpMiFOy7gIbdn2X3JlH7LlQu6qE77E27t43nzyGujAvZEMl0Fir4TXs59Syp-c7Ss6MkCe1eh_VQtzdA3R00o7MHNy2fL_ES_Vkjdf1WcAB4nWQogaw_xZyOptjJxUJfLZyUYkEHpvfiSDx7f6Xr0F9w-gy-2tSemDG7Bp0xGjJJqA3oDZ8KZlR1hINXtCFG9qHIMy0425YFsGJY8nsTyZ2ULFlP2aeH1nnvUY_3O9r7KN_hKZhauxn5qkV5aSY1owVH9GDraYyRf-5JxpqVVAiovkzoqwa5YJXlMgflbK1S-004q_vtlNO2E9Wijy6qjiNUoot3QKybZogrumKSAuuvZwRRtAbvDdt7pZFyqxfEp6G7ofjR-MHNlinTq9rku2zu3znlFWI7j-nny465XasRL04KJXzjHXOAjpc0Ww4Ns-xnS24kVACj_ioBQ4XWSsHMUSdZfttGBWE4AL-64Ll7avyn9U64iEf9grCct3Hu1Dub8wMcwbXzjN7OPb3FLTlT8-zLTgWFmuMXpI7PV4wYRzt61APV3OCDfoq21XTr9Qn-nTaiNESDsClOvL49ZqYPTwFunCYkfR-jhgH06vc6wdB9XXV5jgIqdD5z1JXv8g4XJV3BTTj5SpGhomM9LkHgkDtZwzqMzJClbtkQArncyzLAKoX2kLx2_8t5u69rqCV6mSVDPwoeiJjVcl0uK8UmnCnk8MvHyN6odT-u_osm7aihSojxqKHBRJxdS3eB3gXq4qdNb8qVMGACMNpH4x_bp0qPvKGCOKJV0Lncer6H3HeLHVbrD6KPvWv5_g8JirNW5RDe5umOhuD1rFJ)

- [Biomolecular Modeling and Design Resources](https://abeebyekeen.com/categories/resources)

- [Understanding AlphaFold – Dame Janet Thornton](https://www.youtube.com/watch?v=lxgaILSZEbU)

- [Leveraging Molecular ML + Property Prediction in Drug Design](https://www.youtube.com/watch?v=wisrT2_EYrA)

- [Geometric Deep Learning for Protein Understanding](https://www.youtube.com/watch?v=h7Rifw0Nuv4)

- [Polaris: Industry-Led Initiative to Critically Assess ML for Real-World Drug Discovery](https://www.youtube.com/watch?v=Tsz_T1WyufI)

- [Efficiently Exploring Combinatorial Perturbations From High Dimensional Observation](https://www.youtube.com/watch?v=8ZjqsgsPV_0)

- [Towards Rational Drug Design with AlphaFold 3](https://www.youtube.com/watch?v=AE35XCN5NuU)

- [How AI and accelerated computing are transforming drug discovery](https://www.ft.com/partnercontent/nvidia/how-ai-and-accelerated-computing-are-transforming-drug-discovery.html)

- [Review and discussion of AlphaFold3](https://www.youtube.com/watch?v=qjFgthkKxcA)

- [Understanding & discovering fold-switching proteins by combining AlphaFold2](https://www.youtube.com/watch?v=rgGceDDnIEo)

- [Accelerating drug discovery with AI](https://www.youtube.com/watch?v=-hl0jpwWbV4)

- [Intro to ML in Drug Discovery: Principles & Applications](https://www.youtube.com/watch?v=j-oLfEm7xD8)

- [Introduction to AI in Drug Discovery](https://www.youtube.com/watch?v=7NgPGh0E0XE)

- [AlphaFold3: A foundation model for biology](https://harrisbio.substack.com/p/alphafold3-a-foundation-model-for)

- [[Paper] Deep Learning for Drug Discovery and Cancer Research: Automated Analysis of Vascularization Images](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7904235)

- [[Paper] Deep learning in drug discovery: an integrative review and future challenges](https://github.com/imteekay/machine-learning-research/blob/master/papers/deep-learning-in-drug-discovery-an-integrative-review-and-future-challenges/paper.pdf)

- [DeepMind AlphaFold 3](https://www.youtube.com/watch?v=Mz7Qp73lj9o&ab_channel=TwoMinutePapers)

- [[Course] Introduction to Genomic Data Science](https://www.edx.org/learn/bioinformatics/the-university-of-california-san-diego-introduction-to-genomic-data-science)

- [Generative models for molecular discovery: Recent advances and challenges](https://wires.onlinelibrary.wiley.com/doi/epdf/10.1002/wcms.1608)

- [Generative Models of Molecular Structures](https://www.youtube.com/watch?v=15bHUOjp6IU&list=PLoVkjhDgBOt3NyXcTGg_fi-H8qBzNnKgk&index=15)

- [Opportunities and obstacles for deep learning in biology and medicine](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5938574/pdf/rsif20170387.pdf)

- [Ten quick tips for machine learning in computational biology](https://biodatamining.biomedcentral.com/articles/10.1186/s13040-017-0155-3)

- [Machine learning and complex biological data](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1689-0)

- [A guide to machine learning for biologists](https://hfenglab.org/NRev21.pdf)

- [Next-Generation Machine Learning for Biological Networks](https://www.cell.com/action/showPdf?pii=S0092-8674%2818%2930592-0)

### Databases

- [Penn Machine Learning Benchmarks](https://epistasislab.github.io/pmlb)

### Lists

- [Papers on machine learning for proteins](https://github.com/yangkky/Machine-learning-for-proteins)

- [Papers on Protein Design using Deep Learning](https://github.com/Peldom/papers_for_protein_design_using_DL)

## Science

### Fundamentals

- [AP Biology](https://www.khanacademy.org/science/ap-biology)

- [AP Chemistry](https://www.khanacademy.org/science/ap-chemistry-beta)

- [Intro to Biology](https://www.khanacademy.org/science/biology)

- [Intro to Chemistry](https://www.khanacademy.org/science/chemistry)

- [Organic Chemistry](https://www.khanacademy.org/science/organic-chemistry)

- [Introductory Biology](https://ocw.mit.edu/courses/7-016-introductory-biology-fall-2018)

- [Molecular Biology - Part 1: DNA Replication and Repair](https://www.edx.org/learn/molecular-biology/massachusetts-institute-of-technology-molecular-biology-part-1-dna-replication-and-repair)

- [Introduction to Biology - The Secret of Life](https://www.edx.org/learn/biology/massachusetts-institute-of-technology-introduction-to-biology-the-secret-of-life)

### Science

- [AI Case Studies for Natural Science Research](https://www.youtube.com/watch?v=rfPQ2y857eM&ab_channel=MicrosoftResearch)

- [How AI Is Unlocking the Secrets of Nature and the Universe](https://www.youtube.com/watch?v=0_M_syPuFos&ab_channel=TED)

- [Will AI Spark the Next Scientific Revolution?](https://www.youtube.com/watch?v=7wznuB0sKlw)

### Cancer

- [Introduction to the Biology of Cancer](https://www.coursera.org/learn/cancer)

- [Understanding Prostate Cancer](https://www.coursera.org/learn/prostate-cancer)

- [Understanding Cancer Metastasis](https://www.coursera.org/learn/cancer-metastasis)

- [Ask a Researcher: Working in a Cancer Research Lab](https://www.youtube.com/watch?v=YJ8Fk6iLxdg&list=TLPQMjUwNDIwMjIX07N_vVhBIQ&index=5&ab_channel=NationalCancerInstitute)

- [What Causes Cancer?](https://www.youtube.com/watch?v=UlHK3Y_c5Wo&ab_channel=UniversityofCaliforniaTelevision%28UCTV%29)

- [What is Cancer?](https://www.youtube.com/watch?v=2X5kw3mVk08&ab_channel=UniversityofCaliforniaTelevision%28UCTV%29)

- [How is Cancer Diagnosed?](https://www.youtube.com/watch?v=oSOJbu5uqJE&ab_channel=UniversityofCaliforniaTelevision%28UCTV%29)

- [Cancer: Winning the War](https://www.youtube.com/playlist?list=PL504E935D23E00B4B)

- [The Emperor of All Maladies: A Biography of Cancer](https://www.youtube.com/watch?v=D4BGYf2Nkks&ab_channel=GBHForumNetwork)

- [Regina Barzilay: Deep Learning for Cancer Diagnosis and Treatment](https://www.youtube.com/watch?v=x0-zGdlpTeg&ab_channel=LexFridman)

- [Tumour heterogeneity and resistance to cancer therapies](https://www.nature.com/articles/nrclinonc.2017.166)

- [Porque mesmo com a ciência avançando tanto, ainda não temos uma cura para o câncer?](https://threadreaderapp.com/thread/1512474043290632194.html)

### Genetics

- [Cell Biology: Transport and Signaling](https://www.edx.org/learn/cellular-biology/massachusetts-institute-of-technology-cell-biology-transport-and-signaling)

- [Introduction to Genomic Technologies](https://www.coursera.org/learn/introduction-genomics)

- [Classical papers in molecular genetics](https://www.coursera.org/learn/papers-molecular-genetics)

- [Genetics: The Fundamentals](https://www.edx.org/learn/genetics/massachusetts-institute-of-technology-genetics-the-fundamentals)

- [[Course] Genetics: The Fundamentals](https://www.edx.org/learn/genetics/massachusetts-institute-of-technology-genetics-the-fundamentals)

- [[Course] Genetics: Analysis and Applications](https://www.edx.org/learn/genetics/massachusetts-institute-of-technology-genetics-analysis-and-applications)

- [[Course] Genomic Medicine Gets Personal](https://www.edx.org/learn/bioinformatics/georgetown-university-genomic-medicine-gets-personal)

- [[Course] Essentials of Genomics and Biomedical Informatics](https://www.edx.org/learn/biomedical-sciences/israelx-essentials-of-genomics-and-biomedical-informatics)

- [Genomics Papers](https://github.com/jtleek/genomicspapers)

- [Jennifer Doudna: The Exciting Future of Genome Editing](https://www.youtube.com/watch?v=D4FOtJoqoKM)

### Computational Biology

- [Foundations of Computational and Systems Biology](https://ocw.mit.edu/courses/7-91j-foundations-of-computational-and-systems-biology-spring-2014)

- [Bioinformatics](https://seen-politician-a47.notion.site/ccd895cfaee94849bc9c405a4143b4f5?v=8ca8b89a8be54d7c800a1dfe9780abfc)

- [Understanding life via computational bioinformatics](https://www.youtube.com/watch?v=KH_ZxNu9vj4&ab_channel=OrangeCountyACMChapter)

### Precision Health

- [Defining precision health: a scoping review protocol](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7888329/pdf/bmjopen-2020-044663.pdf)

### Meta

- [Fei-Fei Li & Demis Hassabis: Using AI to Accelerate Scientific Discovery](https://www.youtube.com/watch?v=KHFmIknP_Hc&ab_channel=StanfordHAI)

- [Science is the great giver](https://www.gatesnotes.com/European-Innovation)

- [The Age of AI has begun](https://www.gatesnotes.com/The-Age-of-AI-Has-Begun)

- [Writing in the Sciences](https://www.coursera.org/learn/sciwrite)

- [How to read and understand a scientific paper: a guide for non-scientists](https://blogs.lse.ac.uk/impactofsocialsciences/2016/05/09/how-to-read-and-understand-a-scientific-paper-a-guide-for-non-scientists)

- [Demis Hassabis, AI to Accelerate Scientific Discovery](https://www.youtube.com/watch?v=u1dl_keFK4w&ab_channel=Axial)

- [Demis Hassabis, AI for Science](https://www.youtube.com/watch?v=Q2JmdyqLqiw&ab_channel=Axial)

### Central Resources

- [Armando Hasudungan](https://www.youtube.com/user/armandohasudungan)

- [John Gilmore M.D.](https://www.youtube.com/channel/UCqBho4rDGlST_PY5I2Bh9yQ)

- [Dr. Najeeb Lectures](https://www.youtube.com/channel/UCPHpx55tgrbm8FrYYCflAHw)

- [MedCram - Medical Lectures Explained CLEARLY](https://www.youtube.com/channel/UCG-iSMVtWbbwDDXgXXypARQ)

- [nabil ebraheim](https://www.youtube.com/user/nabilebraheim)

- [Strong Medicine](https://www.youtube.com/channel/UCFq5vPnNRNNNysLrktz4aSw)

- [Cancer Research Demystified](https://www.youtube.com/c/CancerResearchDemystified/featured)

- [Cancer.Net](https://www.cancer.net)

- [Books on Computational Molecular Biology](https://mitpress.mit.edu/books/series/computational-molecular-biology)

- [Obenauf Lab](https://www.obenauflab.com)

### Science: Q&A

- [As a computer science graduate student, I am motivated to do cancer research. How significantly can computer scientists contribute to cancer research? Where are such research institutes where I can pursue a PhD?](https://www.quora.com/As-a-computer-science-graduate-student-I-am-motivated-to-do-cancer-research-How-significantly-can-computer-scientists-contribute-to-cancer-research-Where-are-such-research-institutes-where-I-can-pursue-a-PhD)

- [How can I contribute to cancer research as a computer engineering student if I have basic knowledge in artificial Intelligence?](https://www.quora.com/How-can-I-contribute-to-cancer-research-as-a-computer-engineering-student-if-I-have-basic-knowledge-in-artificial-Intelligence)

- [What kind of knowledge gaps in molecular biology make cancer a big problem for researchers?](https://www.quora.com/What-kind-of-knowledge-gaps-in-molecular-biology-make-cancer-a-big-problem-for-researchers)

## Projects

- [Breast Cancer Prediction: Predicting whether breast cancer tumors are malignant or benign](https://github.com/imteekay/breast-cancer-prediction)

## Careers

- [ML Researcher at Borealis AI](careers/ml-researcher-borealis-ai.pdf)

- [Crushing your interviews for Data Science and Machine Learning Engineering roles](https://building.nubank.com.br/crushing-your-interviews-for-data-science-and-machine-learning-engineering-roles-8-practical-tips)

- [Research Scientist, Health AI — OpenAI](careers/research-scientist-health-ai-openaI.pdf)

## People

- [Andrej Karpathy](https://karpathy.ai)

- [Alex Krizhevsky](https://www.cs.toronto.edu/~kriz)

- [Geoffrey E. Hinton](https://www.cs.toronto.edu/~hinton)

- [Rob Tibshirani](https://tibshirani.su.domains)

- [Trevor Hastie](https://hastie.su.domains)

- [Daniela Witten](https://www.danielawitten.com)

- [Hattie Zhou](http://hattiezhou.com)

- [Chelsea Voss](https://csvoss.com)

- [Lillian](https://lilianweng.github.io)

- [Christopher Olah](https://colah.github.io)

- [Alex Irpan](https://www.alexirpan.com)

- [Gwern Branwen](https://gwern.net)

- [Jonathan Taylor](https://jtaylor.su.domains)

- [Apoorva Srinivasan](https://www.apoorva-srinivasan.com)

- [Susan Zhang](https://suchenzang.github.io)

- [Michael Chang](https://mbchang.github.io)

- [Jan Leike](https://jan.leike.name)

- [Xiao Ma](https://maxiao.info)

- [Gabriele Corso](https://gcorso.github.io)

- [Falk Hoffmann](https://medium.com/@falk_hoffmann)

- [Sara Hooker](https://www.sarahooker.me)

- [Mario Geiger](https://mariogeiger.ch)

- [Charlotte Bunne](https://www.bunne.ch)

- [Charlie Harris](https://cch1999.github.io)

- [Yuanqi Du](https://yuanqidu.github.io)

- [Sophia Sanborn](https://www.sophiasanborn.com)

- [Omar Sanseviero](https://osanseviero.github.io/hackerllama/blog)

- [Simon Willison](https://simonwillison.net)

- [Hamel Husain](https://hamel.dev)

- [Philipp Schmid](https://www.philschmid.de)

- [Eugene Yan](https://eugeneyan.com/writing)

- [Chip Huyen](https://huyenchip.com/blog)

- [Chenru Duan](https://www.crduan.com)

- [Jeff Guo](https://guojeff.github.io)

- [Arian Jamal](https://jamasb.io)

- [Joseph Suárez](https://jsuarez5341.github.io)

- [Andrew Ng](http://andrewng.org)

- [Mathematics behind Deep learning](https://mathblog.vercel.app)

- [Kevin Kaichuang Yang](https://yangkky.github.io)

- [Terence Parr](https://explained.ai)

- [Penny Xu](https://penny-xu.github.io)

- [Amy X. Lu](https://amyxlu.github.io)

- [Benjamin Bloem-Reddy](https://www.stat.ubc.ca/~benbr)

- [Quanhan (Johnny) Xi](https://xijohnny.github.io)

- [Eric Horvitz](https://erichorvitz.com)

- [Rinaldo Montalvão](https://www.linkedin.com/in/rwmontalvao)

- [Joanne Peng](https://www.joannepeng.com)

- [Sarah Alamdari](https://www.sarahalamdari.com)

- [Lorin Crawford](https://www.lorincrawford.com)

- [Ava Amini](https://avaamini.com)

- [Alex Lu](https://www.alexluresearch.com)

- [Kevin Kaichuang Yang](https://yangkky.github.io)

## Research & Laboratories

- [Microsoft Health Futures](https://www.microsoft.com/en-us/research/lab/microsoft-health-futures)

## License

[MIT](/LICENSE) © [TK](https://iamtk.co)