imteekay/machine-learning-research

✨ AI/ML Research

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ML Research Table of Contents ML Research Table of Contents Learning Roadmap Mathematics General Math How to learn mathematics Linear Algebra Statistics Calculus Optimization Programming Algorithms Python ML Engineering Distributed Systems Artificial Intelligence Data Science Machine Learning Multimodal Machine Learning Geometric Machine Learninig Deep Learning Computer Vision Transformers Large Language Models (LLMs) Generative AI Deep Reinforcement Learning Causal Inference PyTorch ML/AI & Healthcare ML for Clinical Knowledge ML/AI & Biology Podcasts Questions and Answers Databases Meta / Lists Science Fundamentals Science Biology Cancer Genetics Computational Biology Precision Health Meta Science: Q&A Careers How to: Interview Prep How to: Coding prep Jobs Research Projects Community People Research & Laboratories Communities Central Resources Companies License Learning Roadmap Machine Learning Roadmap 2022 How to learn AI and ML Recommendations by Ilya Sutskever The Ultimate Guide to Learning About Artificial Intelligence How I Would Learn Bioinformatics From Scratch 12 Years Later: A Roadmap Machine Learning and Deep Learning in Python using Scikit-Learn and PyTorch From Logistic Regression to Transformers: Learning Path Palindrome: ML Library Mathematics General Math Data Science Math Skills Mathematics of Big Data and Machine Learning Mathematics for Machine Learning How to get from high school math to cutting-edge ML/AI The Complete Mathematics of Neural Networks and Deep Learning Math Academy Why Should You Learn Mathematics for ML [Book] Mathematical Foundations of Machine Learning How to learn mathematics How to study math — Jo Boaler How To Self-Study Math How to learn physics & math Best Way to Learn Math How to learn math — Jordan Ellenberg Learn Mathematics from START to FINISH How to Learn Math Why Learn Discrete Math? Linear Algebra Linear Algebra at MIT Khan Academy Linear Algebra Linear algebra cheat sheet for deep learning [Course] Essence of linear algebra [Course] Linear Algebra Crash Course Linear Algebra Tutorial Tiled Matrix Multiplication The Big Picture of Linear Algebra [Interview] Gilbert Strang: Linear Algebra Mathematics for Machine Learning - Linear Algebra Linear Algebra for Data Science Introduction to Linear Algebra for Applied Machine Learning with Python [Book] Linear Algebra for Data Science Statistics Khan Academy Probability Khan Academy Statistics and probability Inferential Statistics Introduction to Statistics The better way to do statistics A complete guide to box plots Probability and Statistics Probability for Computer Scientists Introduction to Statistics and Data Analysis Probability Bootcamp [Book Notebooks] Probabilidade e Estatística: teoria, simulação e dados [Book] Probabilidade e Estatística: teoria, simulação e dados Calculus [Course] Essence of calculus Khan Academy Multivariable Calculus Khan Academy Differential Calculus Calculus Applied Mathematics for Machine Learning - Multivariate Calculus Optimization Convex Optimization Understanding Gradient Descent Programming Algorithms A&DS — Pavel Mavrin Python Practical Python Programming 100 Numpy Exercises From Python to Numpy ML Engineering Introduction to Data Intensive Engineering Machine Learning in Production: Why You Should Care About Data and Concept Drift Monitoring Machine Learning Models in Production From Model-centric to Data-centric AI Making Deep Learning Go Brrrr From First Principles ML Experiment Tracking: What It Is, Why It Matters, and How to Implement It ML Model Baselines [Paper] Large Scale Distributed Deep Networks [Book] Machine Learning Systems (PDF) [Article] A Meticulous Guide to Advances in Deep Learning Efficiency over the Years [Video] Low Level Technicals of LLMs Distributed Systems What even is distributed systems 6.5840: Distributed Systems course Blogs Distributed Systems Course | Distributed Computing @ University Cambridge Artificial Intelligence Data Science Fundamental Python Data Science Libraries: Numpy Fundamental Python Data Science Libraries: Pandas Fundamental Python Data Science Libraries: Matplotlib Fundamental Python Data Science Libraries: Scikit-Learn Data Engineering Roadmap How to build a data science project from scratch Introdução à Análise de Dados para pesquisa no SUS Machine Learning Intro to Machine Learning Intermediate Machine Learning Introduction to Machine Learning Course Learning Math for Machine Learning Machine Learning at CMU Bishop Keynotes on ML Machine Learning Guides by Google Machine Learning Crash Course by Google Facebook Field Guide to Machine Learning Um pequeno guia para Data Science / Machine Learning Machine Learning for All Reinforcement Learning Machine Learning Crash Course with TensorFlow APIs Backpropagation from the ground up Understanding Machine Learning: From Theory to Algorithms CS229 Lecture Notes A theory-heavy intro to machine learning ML Code Challenges Machine learning in Python with scikit-learn Introduction to Algorithms and Machine Learning How to actually learn AI/ML: Reading Research Papers Machine Learning Fundamentals: Bias and Variance Machine Learning Fundamentals: Cross Validation Machine Learning Specialization by Andrew Ng AI Fundamentals Artificial Intelligence [Paper] Hyper-Parameter Optimization: A Review of Algorithms and Applications [Paper] How to avoid machine learning pitfalls: a guide for academic researchers [Paper] Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning Árvore de decisão [Paper] How to avoid machine learning pitfalls: a guide for academic researchers Deep-ML The ML Roadmap Why is machine learning 'hard'? A Gentle Introduction to Machine Learning Theory Stanford Intro to Machine Learning ML Resources Interpretable Machine Learning Intro to Data Science Support Vector Machines Part 1 (of 3): Main Ideas Support Vector Machines Part 2: The Polynomial Kernel Support Vector Machines Part 3: The Radial (RBF) Kernel MIT — Learning: Support Vector Machines Support Vector Machines | Stanford CS229 Preprocessing for Machine Learning in Python Computer Science for Artificial Intelligence Machine Learning courses Machine Learning Crash Course with TensorFlow APIs Machine Learning Stanford Course Machine Learning with Python Math for Machine Learning with Python Machine Learning with Python: from Linear Models to Deep Learning Probabilistic Machine Learning The Elements of Statistical Learning Pattern Recognition and Machine Learning Python Machine Learning Python Data Science Handbook Think Stats: Exploratory Data Analysis in Python The Orange Book of Machine Learning [Book] Hyperparameter Optimization in Machine Learning [Playbook] Tuning Deep Learning Models [Course] Machine Learning Masters Degree — IMPA Multimodal Machine Learning [List] Awesome Multimodal Machine Learning [Course] CMU - Multimodal Machine Learning [Paper] Overview of Multimodal Machine Learning [Paper] Foundations & Trends in Multimodal Machine Learning: Principles, Challenges, and Open Questions Geometric Machine Learninig A Gentle Introduction to Graph Neural Networks [Book] Graph Neural Networks [Paper] Everything is Connected: Graph Neural Networks [Article] Graph Convolutional Networks [Video] Theoretical Foundations of Graph Neural Networks [Book] Geometric Deep Learning Deep Learning Intro to Deep Learning MIT Introduction to Deep Learning Language Modeling from Scratch Deep Learning Book Deep Learning with Python Dive into Deep Learning Intro to Deep Learning Intro to Deep Learning with PyTorch Deep Learning Research and the Future of AI [Paper] Sequence to Sequence Learning with Neural Networks Demystifying deep reinforcement learning A Review of: Human-Level Control through deep Reinforcement Learning [Paper] Mastering the game of Go without human knowledge AlphaGo Zero: Starting from scratch Neural Networks The Principles of Deep Learning Theory Why do tree-based models still outperform deep learning on tabular data? MIT 6.S191: Introduction to Deep Learning Deep Learning NYU Building Neural Networks from Scratch The Matrix Calculus You Need For Deep Learning Paper Convolution is Matrix Multiplication Neural Networks and Deep Learning — Course 1 Improving Deep Neural Networks — Course 2 Structuring Machine Learning Projects — Course 3 Convolutional Neural Networks — Course 4 Sequence Models — Course 5 Understanding Deep Learning Book Club Dive into Deep Learning TABPFN: A transformer that solves small tabular classification problems in a second A Matemática das Redes Neurais Introdução a Redes Neurais e Deep Learning How do neural networks learn features from data? Neural Networks: Zero to Hero Building A Neural Network from Scratch with Mathematics and Python Neural Network from Scratch Feedforward Neural Networks in Depth, Part 1: Forward and Backward Propagations Feedforward Neural Networks in Depth, Part 2: Activation Functions Feedforward Neural Networks in Depth, Part 3: Cost Functions [Paper] Three Decades of Activations: A comprehensive survey of 400 activation functions for neural networks Famous Deep Learning Papers [Paper] Decentralized Diffusion Models Deep Learning for Data Science (DL4DS) Recurrent Neural Networks cheatsheet Deep Learning with PyTorch A deep-dive on the entire history of deep-learning History of Deep Learning Intuitions on Language Models & Shaping the Future of AI from the History of Transformer The Unreasonable Effectiveness of Recurrent Neural Networks Language Modeling From Scratch The Recurrent Neural Network - Theory and Implementation of the Elman Network and LSTM [Article] A Brief History of Large Language Models [Article] Tokenization — A Complete Guide [Article] Word Embeddings with word2vec from Scratch in Python [Article] Self-Attention Explained with Code [Article] A Complete Guide to BERT with Code [Video] The physics behind diffusion models [Paper] An end-to-end attention-based approach for learning on graphs [Book] Learning Deep Representations of Data Distributions [Article] Large Language Model Optimization: Memory, Compute, and Inference Techniques [Book] Learning Deep Representations of Data Distributions [Paper Video] Continuous Thought Machine Deep Dive [Course] Deep Learning PaperCode Language model alignment-focused deep learning curriculum Deep Learning Alchemy Course Computer Vision [Course] Deep Learning for Computer Vision Transformers [Course] Large Language Models [Paper] A Generalization of Transformer Networks to Graphs Video Lecture Biomedical Transformers Glossary of Deep Learning: Word Embedding How I Learned to Stop Worrying and Love the Transformer How Transformer LLMs Work Introduction to Transformers Overview of Transformers Recurrent Neural Networks, Transformers, and Attention Stanford CS25 - Transformers United Stanford CS25: V4 I Overview of Transformers The Illustrated Transformer The math behind Attention: Keys, Queries, and Values matrices Transformer Neural Networks Derived from Scratch Transformers are Graph Neural Networks Transformers from Scratch Visualizing A Neural Machine Translation Model Visualizing transformers and attention [Article] The Annotated Transformer [Article] Attention Mechanism: From Math to GPU [Article] Visualizing Parallelism in Transformer Large Language Models (LLMs) [Course] Large Language Models (LLMs) [Book] How to Scale Your Model [Course] CME 295 - Transformers & Large Language Models [Course] Foundation models and generative AI Generative AI The Principles of Diffusion Models [Notebook] Mathematics of Generative AI Book Deep Reinforcement Learning [Course] Deep Reinforcement Learning Spinning Up in Deep RL Reinforcement Learning: An Overview A vision researcher’s guide to some RL stuff: PPO & GRPO [Course] DeepMind Reinforcement Learning An Ultra Opinionated Guide to Reinforcement Learning Reinforcement Learning Quickstart Guide A Reinforcement Learning Guide Reinforcement Learning Reinforcement Learning: An Introduction Understanding reinforcement learning for model training from scratch [Course] Reinforcement Learning of Large Language Models [Book] Reinforcement Learning from Human Feedback [Course] Mathematical Foundations of Reinforcement Learning [Course] Stanford CS234 Reinforcement Learning [Course] Stanford CS224R Deep Reinforcement Learning [Article] How to Explore to Scale RL Training of LLMs on Hard Problems? [Course] Deep RL by Stanford [Book] Deep Reinforcement Learning in Action [Article] Reinforcement Learning (RL) Guide by Unsloth [Book] Reinforcement Learning: Theory and Python Implementation Key Papers Causal Inference [Article] Causal inference resources [Book] The Book of Why [Book] Causal Inference in Statistics: A Primer [Book] Causal Inference for the Brave and True [Book] Causal Inference in Python [Book] Causal Inference and Discovery in Python [Book] Causal Artificial Intelligence PyTorch PyTorch internals Learn PyTorch for deep learning in a day PyTorch in One Hour: From Tensors to Training Neural Networks on Multiple GPUs ML/AI & Healthcare [Course] Machine Learning for Healthcare AI in Healthcare @ Google Brain Healthcare's AI Future: A Conversation with Fei-Fei Li & Andrew Ng AI and the Future of Health Aplicações de Deep Learning a Genética Daphne Koller: Biomedicine and Machine Learning Data and resource needs for machine learning in genomics Machine Learning para Predições em Saúde Inteligência Artificial em Saúde [Course] Collaborative Data Science for Healthcare [Course] Data Analytics and Visualization in Health Care [Course] Introduction to Applied Biostatistics: Statistics for Medical Research [Paper] Capabilities of Gemini Models in Medicine Journal Club Debate: Capacidades dos modelos Gemini na medicina [Paper] Deep learning methods for drug response prediction in cancer: Predominant and emerging trends [Paper] Machine Learning Prediction of Cancer Cell Sensitivity to Drugs Based on Genomic and Chemical Properties [Paper] Artificial intelligence in healthcare: past, present and future Multimodal Generative AI: the Next Frontier in Precision Health Artificial Intelligence in Healthcare: Past, Present and Future [Paper] The myth of generalisability in clinical research and machine learning in health care [Paper] Sybil: A Validated Deep Learning Model to Predict Future Lung Cancer Risk From a Single Low-Dose Chest Computed Tomography [Paper] Capabilities of Gemini Models in Medicine [Paper] Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine Large Language Models Encode Clinical Knowledge AI Aspirations Healthcare Futures Breast Cancer Prediction: project Training ML Models for Cancer Tumor Classification AI for Business Transformation: Lessons from Healthcare The revolution in high-throughput proteomics and AI [Course] AI for Medicine Specialization Towards Democratization of Subspeciality Medical Expertise Uncovering early predictors of cerebral palsy through the application of machine learning: a case-control study Development and Validation of a Deep Learning Method to Predict Cerebral Palsy From Spontaneous Movements in Infants at High Risk What VCs Look for When Investing in Bio and Healthcare [Paper] Dermatologist-level classification of skin cancer with deep neural networks [Paper] Deep learning for healthcare: review, opportunities and challenges [Paper] Opportunities and obstacles for deep learning in biology and medicine [Paper] Deep Learning in Medical Image Analysis [Paper] CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning [Paper] Medical deep learning—A systematic meta-review [Paper] Scalable and accurate deep learning with electronic health records [Paper] Dermatologist–level classification of skin cancer with deep neural networks [Paper] Deep Learning in Medicine [Paper] Multimodal Healthcare AI: Identifying and Designing Clinically Relevant Vision-Language Applications for Radiology [Paper] Collaboration between clinicians and vision–language models in radiology report generation [Paper] ReXplain: Translating Radiology into Patient-Friendly Video Reports AI for Medical Diagnosis AI for Medical Prognosis AI For Medical Treatment Multimodal, Generative, and Agentic AI for Pathology [Paper] Medical multimodal foundation models in clinical diagnosis and treatment: Applications, challenges, and future directions [Paper] Large language models are less effective at clinical prediction tasks than locally trained machine learning models [Course] Applied Artificial Intelligence for Health Research [Paper] Evaluating EHR Foundation Models for Early Pancreatic Cancer Detection [Paper] DT-Transformer: A Foundation Model for Disease Trajectory Prediction on a Real-world Health System [Paper] Agentic systems for breast cancer treatment recommendations ML for Clinical Knowledge [Paper] Large Language Models Encode Clinical Knowledge ML/AI & Biology [Paper] Machine learning-aided generative molecular design Simulating 500 million years of evolution with a language model Learning to Plan Chemical Syntheses Machine Learning for Genomics MIT Deep Learning in Life Sciences AI Text2Protein Breakthrough Tackles the Molecule Programming Challenge Genomic Language Models: Opportunities and Challenges Melodia: A Python Library for Protein Structure Analysis Biomolecular Modeling and Design Resources Understanding AlphaFold – Dame Janet Thornton Leveraging Molecular ML + Property Prediction in Drug Design Geometric Deep Learning for Protein Understanding Polaris: Industry-Led Initiative to Critically Assess ML for Real-World Drug Discovery Efficiently Exploring Combinatorial Perturbations From High Dimensional Observation Towards Rational Drug Design with AlphaFold 3 How AI and accelerated computing are transforming drug discovery Review and discussion of AlphaFold3 Understanding & discovering fold-switching proteins by combining AlphaFold2 Accelerating drug discovery with AI Intro to ML in Drug Discovery: Principles & Applications Introduction to AI in Drug Discovery AlphaFold3: A foundation model for biology [Paper] Deep Learning for Drug Discovery and Cancer Research: Automated Analysis of Vascularization Images [Paper] Deep learning in drug discovery: an integrative review and future challenges DeepMind AlphaFold 3 [Course] Introduction to Genomic Data Science Generative models for molecular discovery: Recent advances and challenges Generative Models of Molecular Structures Opportunities and obstacles for deep learning in biology and medicine Ten quick tips for machine learning in computational biology Machine learning and complex biological data A guide to machine learning for biologists Next-Generation Machine Learning for Biological Networks AlphaFold3 — What’s next in computational drug discovery? — Part 1 Deep generative models for biomolecular engineering Discovering New Molecules Using Graph Neural Networks AI-Driven Drug Discovery Using Digital Biology Digital Biology with insitro's Daphne Koller AI-First: Daphne Koller’s plan to revolutionize drug discovery [Paper] Generative models for molecular discovery: Recent advances and challenges How AI is saving billions of years of human research time Bioinformatics Toolkit An Overview of Deep Generative Models in Functional and Evolutionary Genomics Transformer Architecture and Attention Mechanisms in Genome Data Analysis: A Comprehensive Review Deep Learning for Genomics: From Early Neural Nets to Modern Large Language Models A review of multimodal deep learning methods for genomic-enabled prediction in plant breeding Deep Generative Models for Drug Design and Response So where are we with deep learning for biochem? A review of transformers in drug discovery and beyond Transformer Architecture and Attention Mechanisms in Genome Data Analysis: A Comprehensive Review Biomedical Transformers Large language models for genomics Data Science for Biologists Using state-of-the-art AI models to power drug-design AlphaGenome: advancing regulatory variant effect prediction with a unified DNA sequence model AlphaGenome: AI for better understanding the genome Alphafold [Paper] Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences [Article] ProteinML 101 [Paper] Flow Matching for Generative Modeling [Paper] SE(3)-Stochastic Flow Matching for Protein Backbone Generation [Article] Diving Into Protein Design with SE(3) Flow Matching [Article] Protein Embeddings: Unlocking Protein Secrets with Deep Learning; ESM, and ProteinBERT [Paper] Transfer learning with graph neural networks for improved molecular property prediction in the multi-fidelity setting [Paper] Biological structure and function emerge from scalingunsupervised learning to 250 million protein sequences [Video] The ESM-1b protein language model [Paper] AI-driven protein design [Paper] Sequence modeling and design from molecular to genome scale with Evo [Paper] Learning the protein language: Evolution, structure, and function [Paper] DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genome [Paper] Evolutionary-scale prediction of atomic-level protein structure with a language model [Paper] ProGen2: Exploring the boundaries of protein language models [Paper] A Deep Learning Approach to Antibiotic Discovery [Paper] gReLU: a comprehensive framework for DNA sequence modeling and design [Paper] DeepSomatic: Accurate somatic small variant discovery for multiple sequencing technologies (code) [Video] AI for molecular modeling and protein design [Video] AI Virtual Cell and Spatial Proteomics [Article] No, AlphaFold has not completely solved protein folding [Video] Making biology programmable [Video] DeepMind AlphaFold [Video] AlphaFold 3 deep dive [Video] AlphaFold - Veritasium [Article] How to Build the AI Virtual Cell [Paper] Open-source protein structure AI aims to match AlphaFold [Video] Behind the scenes at Isomorphic Labs [Paper] Causal Inference for Computational Biology [Video] The State of AI in Drug Discovery 2025 [Article] Gaps and Risks of AI in the Life Sciences [Paper] Opportunities and obstacles for deep learning in biology and medicine [Notebook] Deep Learning in Genomics Primer [Paper] Designing synthetic regulatory elements using the generative AI framework DNA-Diffusion [Paper] The AI revolution: how multimodal intelligence will reshape the oncology ecosystem [Paper] A multimodal machine learning model for the stratication of breast cancer risk [Course] MO640 - Biologia Computacional [Thesis] Generative Models for Real-World Drug Discovery [Book] Deep Learning for Molecules & Materials Protein Structure & Structural Bioinformatics Multiple Sequence Alignment in AlphaFold2 Encoder-only Transformers as Continuous Fuzzy String Matching Structure Prediction without Multiple Sequence Alignment in ESMFold [Article] On Training Data for Bio AI Models [Article] Genome Foundation Models [Paper] MAMMAL - Molecular Aligned Multi-Modal Architecture and Language for biomedical discovery The biggest AI breakthrough in medicine & drug discovery [Course] Learn Computer-Aided Drug Design from First Principles Podcasts Data Science, Past, Present and Future with Hilary Mason Questions and Answers Andrew Ng's answer on "How should you start a career in Machine Learning?" How do I learn mathematics for machine learning? How do I learn machine learning? Databases Penn Machine Learning Benchmarks Meta / Lists Papers on machine learning for proteins Papers on Protein Design using Deep Learning How To Read AI Research Papers Effectively fast.ai vs. deeplearning.ai Science Fundamentals AP Biology AP Chemistry Intro to Biology Intro to Chemistry Organic Chemistry Introductory Biology Molecular Biology - Part 1: DNA Replication and Repair Introduction to Biology - The Secret of Life Science AI Case Studies for Natural Science Research How AI Is Unlocking the Secrets of Nature and the Universe Will AI Spark the Next Scientific Revolution? Biology Cell biology by the numbers Systems Biology and Biotechnology MIT Systems Biology The Most Important Concept in Biology [Course] MIT 7.01SC Fundamentals of Biology Cancer Introduction to the Biology of Cancer Understanding Prostate Cancer Understanding Cancer Metastasis Ask a Researcher: Working in a Cancer Research Lab What Causes Cancer? What is Cancer? How is Cancer Diagnosed? Cancer: Winning the War The Emperor of All Maladies: A Biography of Cancer Regina Barzilay: Deep Learning for Cancer Diagnosis and Treatment Tumour heterogeneity and resistance to cancer therapies Porque mesmo com a ciência avançando tanto, ainda não temos uma cura para o câncer? Genetics Cell Biology: Transport and Signaling Introduction to Genomic Technologies Classical papers in molecular genetics Genetics: The Fundamentals [Course] Genetics: The Fundamentals [Course] Genetics: Analysis and Applications [Course] Genomic Medicine Gets Personal [Course] Essentials of Genomics and Biomedical Informatics Genomics Papers Jennifer Doudna: The Exciting Future of Genome Editing Computational Biology Foundations of Computational and Systems Biology Bioinformatics Understanding life via computational bioinformatics [Book] Introduction to Protein Structure [Book] Molecular Modelling: Principles and Applications [Book] Structural Bioinformatics [Book] Computational Structural Biology [Book] Molecular Modelling and Simulation [Book] Computational Methods for Protein Structure Prediction and Modeling [Book] Understanding Molecular Simulation [Book] The Art of Molecular Dynamics Simulation [Book] Protein Actions: Principles and Modeling [Book] Computational Protein Design [Article] How anyone can teach themselves bioengineering [Course] Introduction to Bioinformatics and Computational Biology — Harvard Precision Health Defining precision health: a scoping review protocol Meta Fei-Fei Li & Demis Hassabis: Using AI to Accelerate Scientific Discovery Science is the great giver The Age of AI has begun Writing in the Sciences How to read and understand a scientific paper: a guide for non-scientists Demis Hassabis, AI to Accelerate Scientific Discovery Demis Hassabis, AI for Science 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? 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? Careers How to: Interview Prep How I Prepared for DeepMind and Google AI Research Internship Interviews in 2019 65 Machine Learning Interview Questions Data Science Interview Questions Answers Machine Learning Interviews Crushing your interviews for Data Science and Machine Learning Engineering roles Becoming an MLE at FAANG: What you need to know to know about MLE roles and interviews at Google, Meta, and other top companies How To Become an ML Engineer in 2024 How to land a Data Scientist job at your dream company — My journey to Airbnb Data Science Interview Prep Machine Learning Interview Prep ML Design Interview strategy [Video] Google DeepMind ML Engineer Interview Tips [Article] An Unofficial Guide to Prepare for a Research Position Application MLE Interview 2.0: Research Engineering and Scary Rounds DataLemur: Ace the SQL & Data Science Interview [Book] Ace the Data Science Interview [Article] How to Land a Frontier Lab Job [Article] My 2026 Interview Season [Article] ML Job Interviews: The Ultimate Guide [Article] How I Got a Job at DeepMind as a Research Engineer [Article] Notes on the Industry Job Search [Article] Job Hunt as a PhD in AI / ML / RL: How it Actually Happens [Repo] Machine Learning Questions How to: Coding prep LeetCode for ML TorchLeet: Leetcode for PyTorch Deep-ML PyTorch Practice Notebook - ML Interview Preparation 100-Days-Of-ML-Code 65 Machine Learning Interview Questions Jobs ML Researcher at Borealis AI Research Scientist, Health AI — OpenAI ML Engineer — Apple ML Protein Design Machine Learning Scientist - Computational Biology - Deep Genomics (Senior) Machine Learning Scientist, AI Foundation Model Specialist - Deep Genomics (Senior) Principal Scientist, Computational / Systems Biology - Deep Genomics (Senior) Research Scientist - Statistical Genetics - Deep Genomics Research Scientist (Computational Biology, Cells and Tissues) — Isomorphic Labs Research [Article] How I Think About My Research Process: Explore, Understand, Distill [Article] My Research Process: Key Mindsets - Truth-Seeking, Prioritisation, Moving Fast [Article] My Research Process: Understanding and Cultivating Research Taste [Article] A Survival Guide to a PhD [Article] Principles of Effective Research [Article] An Opinionated Guide to ML Research [Article] The Speed of Being Wrong [Article] Research Is Not Engineering at a Slower Speed [Article] An Unofficial Guide to Prepare for a Research Position Application [Article] On research taste [Article] Zen and the Art of AI Research [Video] if you want good taste, you have to eat Projects Breast Cancer Prediction: Predicting whether breast cancer tumors are malignant or benign House Price Regression: Model prediction for house prices SVC/Decision Tree Classifiers PyTorch Fundamentals PyTorch Neural Network Classification PyTorch Computer Vision PyTorch Computer Vision Exercises PyTorch Custom Datasets Community People Andrej Karpathy Alex Krizhevsky Geoffrey E. Hinton Rob Tibshirani Trevor Hastie Daniela Witten Hattie Zhou Chelsea Voss Lillian Christopher Olah Alex Irpan Gwern Branwen Jonathan Taylor Apoorva Srinivasan Susan Zhang Michael Chang Jan Leike Xiao Ma Gabriele Corso Falk Hoffmann Sara Hooker Mario Geiger Charlotte Bunne Charlie Harris Yuanqi Du Sophia Sanborn Omar Sanseviero Simon Willison Hamel Husain Philipp Schmid Eugene Yan Chip Huyen Chenru Duan Jeff Guo Arian Jamal Joseph Suárez Andrew Ng Mathematics behind Deep learning Kevin Kaichuang Yang Terence Parr Penny Xu Amy X. Lu Benjamin Bloem-Reddy Quanhan (Johnny) Xi Eric Horvitz Rinaldo Montalvão Joanne Peng Sarah Alamdari Lorin Crawford Ava Amini Alex Lu Kevin Kaichuang Yang Rocío Mercado Oropeza Pranav Rajpurkar Martin Steinegger Jue Wang Wenhu Chen Melanie Mitchell Jenny Zhang Abhinav Gupta Beidi Chen Avantika Lal Will Connell Danqi Chen Yuqing Du Apoorva Srinivasan Patrick Hsu Brian Hie Andrew Y. K. Foong Karina Nguyen Myle Ott Reza Rezvan Artem Moskalev Hamel Dr. Yejin Kim Jay Alammar Luis Serrano Jiayuan Mao Edward Z. Yang Edward Z. Yang's blog Kejun Ying Xiang Lisa Li Rachel Wu Simon Kohl Karina Nguyen Yehlin Cho Lada Nuzhna Valerie Chen Jian Zhou Maria Chikina Sergey Ovchinnikov James Zou Hanxue Gu Chaitanya K. Joshi Adam Majmudar Qian Huang Lily Liu Yist Yu Seul Lee Zhenqiao Song Wenxian Shi Justas Dauparas Maria Nattestad Cao (Danica) Xiao Sophia Tang So Yeon Tiffany Min Carlos Busso Daniel Fried Yonatan Bisk Paul Liang Alex L. Zhang Sichuan from Total Health Optimization Mengyue Yang Shekoofeh Azizi Pedro O. Pinheiro Ada Fang Shanghua Gao Ailing Zhang Jun Cheng Qiguang Chen Yizhong Wang Yuan Meng Soojung Yang Elana P. Simon Anqi (Angie) Liu Ji Won Park Clara Wong-Fannjiang Vlad Feinberg Linlu Qiu Keya Hu Chanwoo Kim Serena Yeung Haotian Cui Hyunjin Seo Emmanuel Noutahi Aidan Cooper Research & Laboratories Microsoft Health Futures Baker Lab Zhang Lab Zhang lab @ MIT The Programmable Biology Group Medical AI and Computer Vision Lab Communities Machine Learning Reddit NLP Reddit Statistics Reddit Data Science Reddit Machine Learning Quora Topic Statistics Quora Topic Data Science Quora Topic Lee Lab of AI for bioMedical Sciences Lab of big data and predictive analysis in healthcare Jean Fan lab Pranav Rajpurkar The AI Health Podcast Starkly Speaking Multiomics Reading Group Machine Learning for Molecule Simulations & Sampler Reading Group Central Resources Armando Hasudungan John Gilmore M.D. Dr. Najeeb Lectures MedCram - Medical Lectures Explained CLEARLY Nabil Ebraheim Strong Medicine Cancer Research Demystified Cancer.Net Books on Computational Molecular Biology Obenauf Lab Companies Arcadia Calico Altos Labs License MIT © TK

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imteekay/machine-learning-research

✨ AI/ML Research

Jupyter Notebook

456

1,938 commits

updated Sep 24, 2026

www.iamtk.co/ml-ai-learning-path

See the code

README

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