- [49% done] Nimona by Noelle Stevenson
- [4% done] Ready Player One by Ernest Cline {Recommended on Andrej Karpathy's Goodreads reviews}
- El héroe discreto by Mario Vargas Llosa {Personally recommended by C.S. in early 2016 to enrich my Spanish vocabulary and syntax}
- The Goldfinch by Donna Tartt {Personally recommended by H.M.}
- Harry Potter e la pietra filosofale by J. K. Rowling {Personally recommended by H.M. in 2015. While recommended in English, I'll read it in Italian to practice the language}
- The Three-Body Problem by Cixin Liu {Personally recommended by S.S. in late 2017}
- [100% done on 2022-03-15] Zero to One: Notes on Startups, or How to Build the Future by Peter Thiel and Blake Masters
- [100% done on 2022-03-09] Kitchen Confidential: Adventures in the Culinary Underbelly by Anthony Bourdain
- [100% done on 2022-02-22] Emotional Intelligence: Why It Can Matter More Than IQ by Daniel Goleman
- [100% done on 2022-02-13] Salt, Fat, Acid, Heat: Mastering the Elements of Good Cooking by Samin Nosrat
- [51% done] How To Win Friends and Influence People by Dale Carnegie {Personally recommended by M.Y. in 2015}
- [37% done] Lean Customer Development by Cindy Alvarez
- Rocket Surgery Made Easy by Steve Krug {Probably advertised within "Don't Make Me Think"}
- Moonwalking with Einstein by Joshua Foer {Mentioned in passing by M.C. in late 2015}
- Mindset: The New Psychology of Success by Carol Dweck {Advocated by Khan Academy and recommended on Gates Notes blog}
- Barron's AP Biology by Deborah T. Goldberg
- Lean Customer Development by Cindy Alvarez {Recommended by Scott Guthrie in Early 2018}
- Getting Things Done {Recommended by B.S. in 2018}
- Take Back Your Life!: Using Outlook to Get Organized and Stay Organized {Recommended by B.S. in 2018}
- The Organized Mind {Recommended by D.V. in 2018}
- The 4-Hour Chef by Tim Ferriss {Recommended by C.E. in late 2018}
- Mastery by Robert Greene {Recommended by C.E. in late 2018}
- High Output Management by Andrew Grove {Recommended by K.B. in late 2019}
- The Phoenix Project by Gene Kim, Kevin Behr, and George Spafford {Recommended by K.B. in late 2019}
- The Pragmatic Programmer by David Thomas and Andrew Hunt {Recommended by K.B. in late 2019}
- Poor Charlie's Almanack: The Wit and Wisdom of Charles T. Munger {Recommended by D.G.? in 2019}
- Lean In by Sheryl Sandberg
- Buffett: The Making of an American Capitalist by Roger Lowenstein {Personally recommended by M.C. in late 2015}
- The Idea Factory: Bell Labs and the Great Age of American Innovation by Jon Gertner {Recommended on Andrej Karpathy's Goodreads reviews}
- Regenesis: How Synthetic Biology Will Reinvent Nature and Ourselves {Personally recommended by M.C. in early 2016}
- Profiles of the Future by Arthur C. Clarke {Recommended on Andrej Karpathy's Goodreads reviews}
- Superintelligence by Nick Bostrom {Recommended by C.DC. in early 2018}
- Deep Learning by Ian Goodfellow, Aaron Courville, and Yoshua Bengio http://www.deeplearningbook.org/
- Scipy Lecture Notes - https://scipy-lectures.org/
- Reinforcement Learning: An Introduction (e-book) by Richard S. Sutton and Andrew G. Barto - https://webdocs.cs.ualberta.ca/~sutton/book/ebook/the-book.html
- Pattern Recognition and Machine Learning by Christopher Bishop {Recommended to me by several professors}
- Convex Optimization: Algorithms and Complexity by Sébastien Bubeck http://arxiv.org/pdf/1405.4980.pdf
- Good books for all things data - http://multithreaded.stitchfix.com/blog/2016/06/09/ds-books/
- Neural Networks and Deep Learning by Michael Nielsen - http://neuralnetworksanddeeplearning.com/
- Speech and Language Processing by Dan Jurafsky and James H. Martin - https://web.stanford.edu/~jurafsky/slp3/
- Interactive Linear Algebra - http://immersivemath.com/ila/#
- Linguistic Fundamentals for Natural Language Processing: 100 Essentials from Morphology and Syntax by Emily M. Bender
- {Some book on OpenCV in Python}
- {Some book on AWS}
- {Some book on Hadoop, Hive, Spark, and such}
- Discrete Differential Geometry: An Applied Introduction by Keenan Crane - https://www.cs.cmu.edu/~kmcrane/Projects/DDG/paper.pdf
- Phonetics: Transcription, Production, Acoustics, and Perception by Heening Reetz
- Exercises in French Phonics by Francis W. Nachtmann
- The Virtuoso Pianist in 60 Exercises by Charles-Louis Hanon
- Mikrokosmos by Béla Bartók {Personally recommended by R.R. in early 2016}
- Illustrator: Visual Quickstart Guide
- Clean Code: A Handbook of Agile Software Craftsmanship by Robert C. Martin
- Design Patterns: Elements of Reusable Object-Oriented Software by Erich Gamma {Personally recommended by D.R.}
- [27% done] Fluent Python by Luciano Ramalho
- The C Programming Language by Brian Kernighan and Dennis Ritchie
- Dive Into Python by Mark Pilgrim - http://www.diveintopython.net/toc/index.html {Personally recommended by A.N. in early 2016}
- Pro JavaScript Techniques by Russ Ferguson, John Paxton, and John Resig
- The Swift Programming Language {Heard about the book in a Columbia hackathon}
- The Python Language Reference (official free reference) - https://docs.python.org/3/reference/index.html
- Ruby Programming book on WikiBooks - https://en.wikibooks.org/wiki/Ruby_Programming
- Elements of Programming Interviews by Adnan Aziz, Tsung-Hsien Lee, and Amit Prakash
- Cracking the Coding Interview by Gayle Laakmann McDowell
- Italiano Automatico
- Planet Money {Personally recommended by E.Z. in late 2015}
- The New Yorker: Fiction {Personally recommended by E.Z. in late 2015}
- Serial {Personally recommended by E.Z. in late 2015}
- Duke University - Computational Statistics in Python - http://people.duke.edu/~ccc14/sta-663/
- Stanford deep learning tutorial - http://ufldl.stanford.edu/
- Richard Socher, Chris Manning and Yoshua Bengio - 2013 - Tutorial: Deep Learning for NLP (without Magic) - http://www.socher.org/index.php/DeepLearningTutorial/DeepLearningTutorial
- Visualizing CNN architectures side by side with mxnet - Joseph Paul Cohen - http://josephpcohen.com/w/visualizing-cnn-architectures-side-by-side-with-mxnet/
- TensorFlow for Poets by Pete Warden - http://petewarden.com/2016/02/28/tensorflow-for-poets/
- Learn Tensorflow and Deep Learning without a Ph.D. by Martin Görner https://cloud.google.com/blog/big-data/2017/01/learn-tensorflow-and-deep-learning-without-a-phd
- Theano Tutorial - Colin Raffel - http://nbviewer.jupyter.org/github/craffel/theano-tutorial/blob/master/Theano%20Tutorial.ipynb
- Blog Series - Christopher Olah - Neural Network Tutorials - http://colah.github.io/
- Neural Networks (General)
- Neural Networks, Manifolds, and Topology
- Deep Learning, NLP, and Representations
- Understanding LSTM Networks
- Calculus on Computational Graphs: Backpropagation
- Neural Networks, Types, and Functional Programming
- Visualizing Neural Networks
- Visualizing MNIST
- Visualizing Representations
- Inceptionism
- Convolutional Neural Networks
- Conv Nets
- Understanding Convolutions
- Groups and Group Convolutions
- Misc.
- Fanfiction, Graphs, and PageRank
- Data.List Recursion Illustrated
- Visual Information Theory
- Neural Networks (General)
- inFERENce blog by Ferenc Huszár - http://www.inference.vc/
- "How to Trick a Neural Network" and the dangers of black-box models by Julia Evans - http://jvns.ca/blog/2016/05/21/a-few-notes-from-my-pydata-berlin-keynote/?utm_content=bufferfc75f&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer
- Blog Series - Andrej Karpathy - http://karpathy.github.io/
- Apr 25, 2019 - A Recipe for Training Neural Networks - https://karpathy.github.io/2019/04/25/recipe/
- Oct 25, 2015 - What a Deep Neural Network thinks about your #selfie - http://karpathy.github.io/2015/10/25/selfie/
- May 21, 2015 - The Unreasonable Effectiveness of Recurrent Neural Networks - http://karpathy.github.io/2015/05/21/rnn-effectiveness/
- Mar 30, 2015 - Breaking Linear Classifiers on ImageNet - http://karpathy.github.io/2015/03/30/breaking-convnets/
- Sep 2, 2014 - What I learned from competing against a ConvNet on ImageNet - http://karpathy.github.io/2014/09/02/what-i-learned-from-competing-against-a-convnet-on-imagenet/
- Aug 3, 2014 - Quantifying Productivity - http://karpathy.github.io/2014/08/03/quantifying-productivity/
- Jul 3, 2014 - Feature Learning Escapades - http://karpathy.github.io/2014/07/03/feature-learning-escapades/
- Jul 2, 2014 - Visualizing Top Tweeps with t-SNE, in Javascript - http://karpathy.github.io/2014/07/02/visualizing-top-tweeps-with-t-sne-in-Javascript/
- Apr 26, 2014 - Interview with Data Science Weekly on Neural Nets and ConvNetJS - http://karpathy.github.io/2014/04/26/datascience-weekly-interview/
- Nov 27, 2013 - Quantifying Hacker News with 50 days of data - http://karpathy.github.io/2013/11/27/quantifying-hacker-news/
- Nov 23, 2013 - Chrome Extension Programming: Illustrating a Basic Survival Skill with a Twitter Case Study - http://karpathy.github.io/2013/11/23/chrome-extension-programming/
- Oct 22, 2012 - The state of Computer Vision and AI: we are really, really far away. - http://karpathy.github.io/2012/10/22/state-of-computer-vision/
- Apr 27, 2011 - Lessons learned from manually classifying CIFAR-10 - http://karpathy.github.io/2011/04/27/manually-classifying-cifar10/
- Deep Learning Glossary - WildML Blog (by Denny Britz) - http://www.wildml.com/deep-learning-glossary
- 2D Embedding of music genres - http://everynoise.com/engenremap.html
- Colorizing black and white photos with deep learning - https://news.ycombinator.com/item?id=10864801
- otoro.net Blog - http://blog.otoro.net/
- Stan User's Guide and Reference Manual - http://mc-stan.org/documentation/
- Tensorflow tutorials - https://www.tensorflow.org/versions/master/tutorials/index.html
- Torch tutorials - http://torch.ch/
- Notes by Anima Anandkumar on NIPS 2015 workshop on non-convex optimization - http://www.offconvex.org/2016/01/25/non-convex-workshop/
- NIPS 2015 Videos: https://nips.cc/Conferences/2015/Schedule
- Neural Network playground in JS - http://playground.tensorflow.org/
- Microsoft Research NIPS 2015 Videos - http://research.microsoft.com/apps/catalog/default.aspx?t=videos
- A gallery of interesting IPython Notebooks - https://github.com/ipython/ipython/wiki/A-gallery-of-interesting-IPython-Notebooks
- No Free Hunch (Kaggle Blog) - http://blog.kaggle.com/
- Tutorials: http://blog.kaggle.com/category/tutorials/
- Introduction to data analytics with pandas - https://github.com/QCaudron/pydata_pandas
- A Beginner’s Guide to the True Order of SQL Operations - https://blog.jooq.org/2016/12/09/a-beginners-guide-to-the-true-order-of-sql-operations/
- The Probability and Statistics Cookbook - http://statistics.zone/
- Deep RL Bootcamp - Berkeley, CA - https://sites.google.com/view/deep-rl-bootcamp/lectures
- Pandas Dataframe Basics - https://nbviewer.jupyter.org/github/groverpr/learn_python_libraries/blob/master/pandas/pandas_cheatsheet.ipynb
- Understanding and Implementing CycleGAN in TensorFlow by Hardik Bansal and Archit Rathore - https://hardikbansal.github.io/CycleGANBlog/
- 9 Things You Should Know About TensorFlow - https://hackernoon.com/9-things-you-should-know-about-tensorflow-9cf0a05e4995
- Stanford CS 229 Cheatsheets - https://stanford.edu/~shervine/teaching/cs-229.html
- From GAN to WGAN by Lilian Weng - https://lilianweng.github.io/lil-log/2017/08/20/from-GAN-to-WGAN.html
- On Word Embeddings - Part 1 - by Sebastian Ruder - http://ruder.io/word-embeddings-1/index.html
- Playing a game of GANstruction by Helena Sarin - https://thegradient.pub/playing-a-game-of-ganstruction/
- Forward versus Reverse Mode Automatic Differentiation understood as linear system solving by Alec Jacobson - http://www.alecjacobson.com/weblog/?p=4752
- Why building your own Deep Learning Computer is 10x cheaper than AWS - by Jeff Chen - https://medium.com/the-mission/why-building-your-own-deep-learning-computer-is-10x-cheaper-than-aws-b1c91b55ce8c
- Learning Reinforcement Learning (with Code, Exercises and Solutions) - by Denny Britz - Blog post | GitHub Repo
- Step-by-step guides to getting started with Applied Machine Learning - https://machinelearningmastery.com/start-here/
- Building a Recommender System in TensorFlow - Google Cloud documentation - https://cloud.google.com/solutions/machine-learning/recommendation-system-tensorflow-overview
- Machine Learning: The High-Interest Credit Card of Technical Debt - https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/43146.pdf
- TFX: A TensorFlow-Based Production-Scale Machine Learning Platform - http://stevenwhang.com/tfx_paper.pdf
- ML Resources - by Sam Finlayson - https://sgfin.github.io/learning-resources/
- Google AI Blog - https://ai.googleblog.com/
- YOLO object detection with OpenCV - by Adrian Rosebrock - https://www.pyimagesearch.com/2018/11/12/yolo-object-detection-with-opencv/?fbclid=IwAR1XMuExBJey6s6sSU5w96R71CwSStSmIZCW1DOlJbI6kN8P-Kdb5qWilVI
- Stitch Fix Algorithms Tour - https://algorithms-tour.stitchfix.com/
- Sneaky Topology - by 3Blue1Brown - https://www.youtube.com/watch?v=yuVqxCSsE7c&fbclid=IwAR2rgs3fja2xCIcdE9gxIV-0hA78KxVpfHJYWDCJFdoE5KQswPz0J1uCN0g
- Dissecting BERT - https://medium.com/dissecting-bert
- Nine things I wish I had known the first time I came to NIPS - https://medium.com/@jennwv/nine-things-i-wish-i-had-known-the-first-time-i-came-to-nips-b939330661ed
- The intertwined quest for understanding biological intelligence and creating artificial intelligence by Surya Ganguli - https://hai.stanford.edu/news/the_intertwined_quest_for_understanding_biological_intelligence_and_creating_artificial_intelligence/
- Tensor Considered Harmful by Alexander Rush - http://nlp.seas.harvard.edu/NamedTensor?fbclid=IwAR2FusFxf-c24whTSiF8B3R2EKz_-zRfF32jpU8D-F5G7rreEn9JiCfMl48
- Deep Learning Drizzle (GitHub repo with links to lectures etc.) - https://github.com/kmario23/deep-learning-drizzle?fbclid=IwAR2IT8oL8LyLj3NfQBYR2eQ1I6oSlAmr_--DWX0hiMwj2A_RoYozFqfkclA
- Causal inference series by Ferenc Huszár
- ML beyond Curve Fitting: An Intro to Causal Inference and do-Calculus - https://www.inference.vc/untitled/
- Causal Inference 2: Illustrating Interventions via a Toy Example - https://www.inference.vc/causal-inference-2-illustrating-interventions-in-a-toy-example/
- Causal Inference 3: Counterfactuals - https://www.inference.vc/causal-inference-3-counterfactuals/
- Causal Inference 4: Causal Diagrams, Markov Factorization, Structural Equation Models - Coming Soon
- Best Deep Learning Books 2019 - https://blog.floydhub.com/best-deep-learning-books-updated-for-2019/
- OpenCV-Python Cheat Sheet: From Importing Images to Face Detection - https://heartbeat.fritz.ai/opencv-python-cheat-sheet-from-importing-images-to-face-detection-52919da36433
- Hugging Face: State-of-the-Art Natural Language Processing in ten lines of TensorFlow 2.0 - https://medium.com/tensorflow/using-tensorflow-2-for-state-of-the-art-natural-language-processing-102445cda54a
- Machine Learning Interviews: Lessons from Both Sides - FSDL - by Chip Huyen - https://docs.google.com/presentation/d/1MX2V6fTp71j1aztvY5HLYM44iLG4HYMrYd4Dxn6Cxnw/edit#slide=id.g307a3e0e45_0_77
- How to apply machine learning and deep learning methods to audio analysis - by Nikolas Laskaris - https://www.comet.ml/site/how-to-apply-machine-learning-and-deep-learning-methods-to-audio-analysis/
- Perception Engines (abstract paintings that ConvNets recognize) - https://medium.com/artists-and-machine-intelligence/perception-engines-8a46bc598d57
- CNN Explainer - https://poloclub.github.io/cnn-explainer/
- React (JS) documentation https://facebook.github.io/react/docs/getting-started.html
- D3.js Documentation - https://github.com/mbostock/d3/wiki
- Introduction to D3 - https://observablehq.com/@mitvis/introduction-to-d3
- Eric Jang's List of Computer Graphics Resources - https://github.com/ericjang/awesome-graphics
- Khan Academy - Pixar in a Box - https://www.khanacademy.org/partner-content/pixar
- Benedikt Bitterli - The secret life of photons (Blog Post tutorial on light transport) - https://benedikt-bitterli.me/tantalum/
- Equations and Theory behind Google's Filament (Physically-Based Rendering Engine) - https://google.github.io/filament/Filament.md.html
- Computational Photography: From Selfies to Black Holes - https://vas3k.com/blog/computational_photography/
- Building Spectro: a real-time WebGL audio spectrogram visualizer 🎶 - https://github.com/calebj0seph/spectro/blob/master/docs/making-of.md
- What is Peripheral Vision? - http://www.simplifyinginterfaces.com/2008/10/08/what-is-peripheral-vision/
- Google Big Picture Group visualizations - https://research.google.com/bigpicture/
- The future of data visualization - https://www.youtube.com/watch?v=vc1bq0qIKoA
- Designing for accessibility is not that hard - https://www.invisionapp.com/inside-design/designing-accessibility-not-hard/
- UX Maturity Model: From Usable to Delightful - https://uxpamagazine.org/ux-maturity-model/
- Some more CSS comics by Julia Evans - https://jvns.ca/blog/2020/08/10/some-more-css-comics/
- CSS Vocabulary - http://apps.workflower.fi/vocabs/css/en
- Implement Your Own Automatic Differentiation with Julia in ONE day - http://blog.rogerluo.me/2018/10/23/write-an-ad-in-one-day/
- Meeting Julia, a great new alternative for numerical programming - https://medium.com/@nwerneck/meeting-julia-a-great-new-alternative-for-numerical-programming-part-i-benchmarking-c03dd3289493
- A Deep Introduction to Julia for Data Science and Scientific Computing by Chris Rackauckas - http://ucidatascienceinitiative.github.io/IntroToJulia/
- What Is .NET Core? (What Makes It So Special?) by James Hickey - https://www.blog.jamesmichaelhickey.com/What-Makes-NET-Core-So-Special-Why-You-Should-Use-NET-Core/
- 50 Fizzbuzzes by Vi Hart - http://vihart.com/fifty-fizzbuzzes/
- A Guide to Python's Magic Methods by Rafe Kettler - https://rszalski.github.io/magicmethods/
- The Fast Track to Julia (Julia Cheat Sheet) - https://juliadocs.github.io/Julia-Cheat-Sheet/
- Python internals: Arbitrary-precision integer implementation - https://rushter.com/blog/python-integer-implementation/
- Julia: come for the syntax, stay for the speed - https://www.nature.com/articles/d41586-019-02310-3
- Python is eating the world: How one developer's side project became the hottest programming language on the planet - https://www.zdnet.com/article/python-is-eating-the-world-how-one-developers-side-project-became-the-hottest-programming-language-on-the-planet/
- Julia Academy - https://juliaacademy.com/
- Pablo Estrada - Bit y Byte - http://bitybyte.github.io/ {in Spanish}
- Explained Visually - http://setosa.io/ev/
- Build a Compact 4 Node Raspberry Pi Cluster - Make Magazine - https://makezine.com/projects/build-a-compact-4-node-raspberry-pi-cluster/
- What nobody tells you about documentation - Divio Blog - https://www.divio.com/blog/documentation/
- StackOverflow Developer Survey Results 2019 - https://insights.stackoverflow.com/survey/2019
- Pragmatic Unicode - https://nedbatchelder.com/text/unipain.html
- Understanding the Git Workflow - Benjamin Sandofsky - https://sandofsky.com/blog/git-workflow.html
- How to do a code review - Google's Engineering Practices documentation - https://google.github.io/eng-practices/review/reviewer/
- Using Linux Namespaces to Isolate Processes - by Nigel Brown - https://windsock.io/using-linux-namespaces-to-isolate-processes/ {Personally recommended by R.A. in 2019}
- Visual explanation of RAFT distributed consensus algorithm - by The Secret Lives of Data - http://thesecretlivesofdata.com/raft/
- Quantitative Trading Summary - https://blog.headlandstech.com/2017/08/03/quantitative-trading-summary/
- Black-Scholes Formula (d1, d2, Call Price, Put Price, Greeks) - http://www.macroption.com/black-scholes-formula/
- How I made $500k with machine learning and HFT (high frequency trading) - http://jspauld.com/post/35126549635/how-i-made-500k-with-machine-learning-and-hft
- The Ultimate Guide to Bonds - US News - https://money.usnews.com/investing/investing-101/articles/the-ultimate-guide-to-bonds
- How To Write Better Job Descriptions - by Ana Ulin - https://anaulin.org/blog/how-to-write-better-job-descriptions/
- Thinking you do you and I do me - by Layla El Asri - https://speakingmachines.com/blog/2018/12/29/nbspthinking-you-do-you-and-i-do-menbsp
- The Role CS Professors Have in Supporting Marginalized Students - https://medium.com/@victoriajyang/the-role-cs-professors-have-in-supporting-marginalized-students-f61de7dcec50
- Falsehoods Programmers Believe About Names - https://www.kalzumeus.com/2010/06/17/falsehoods-programmers-believe-about-names/
- Mental Models - http://www.defmacro.org/2016/12/22/models.html
- Webtoon Editing Tips: Techniques for Vertical Storytelling - https://www.webtoons.com/en/tiptoon/lozolz/webtoon-editing-tips/viewer?title_no=1268&episode_no=24
- The Holloway Guide to Equity Compensation - https://www.holloway.com/g/equity-compensation
- How the World's Most Difficult Bouldering Problems Get Made - https://www.outsideonline.com/2017711/path-beta-flash-resistance-route-setters
- Laws of Tech: Commoditize Your Complement - by Gwern Branwen - https://www.gwern.net/Complement#2
- Quantum Randomness by Scott Aaronson on Scientific American - https://www.americanscientist.org/article/quantum-randomness
- Sample Performance Based Interviewing (PBI) Questions - US Department of Veterans Afairs - https://www.va.gov/PBI/questions.asp
- How to prepare a presentation - by Alfredo Canziani - https://youtu.be/y4N0_Tvt75s
- Learn to play the following {Personally recommended by J.D. in late 2016}
- Bach 2 Part Inventions and Sinfonias - https://www.youtube.com/watch?v=iHNxdOTFt7c
- Johann Sebastian Bach - Prelude and Fugue in A minor BWV 543 - https://www.youtube.com/watch?v=_7u6jptcpqg
- UCI Machine Learning Repository
- Google Dataset Search
- MuJoCo - Advanced physics simulation
- ATARI Grand Challenge Data
- ImageNet - An image database organized according to the WordNet hierarchy (currently only the nouns), in which each node of the hierarchy is depicted by hundreds and thousands of images
- WordNet - A lexical database for English
- MNIST - Database of handwritten digits
- MS COCO - A large-scale object detection, segmentation, and captioning dataset
- AudioSet - A large-scale dataset of manually annotated audio events
- AutonoVi-Sim - Autonomous Vehicle Simulation Platform with Weather, Sensing, and Traffic control
- Building Parser
- "The 50 Best Free Datasets for Machine Learning"
- Microsoft Research Open Data
- arXiv: http://arxiv.org/
- ArXiv Sanity Preserver: http://www.arxiv-sanity.com/
- ArXiv Vanity: https://www.arxiv-vanity.com/
- Google Dataset Search: https://toolbox.google.com/datasetsearch
- GitXiv: http://gitxiv.com/
- Papers with Code (website): https://paperswithcode.com/
- Papers with Code (GitHub version): https://github.com/zziz/pwc
- PCA News: http://www.pca-news.com/
- Model Zoo: https://modelzoo.co/
- User-friendly NIPS paper search:
- Hugo Larochelle's Notes on Research Papers: https://twitter.com/hugo_larochelle/timelines/639067398511968256
- Short Science: http://www.shortscience.org/
- Fermat's Library: http://fermatslibrary.com/
- Paperscape: http://paperscape.org/
- OpenReview: https://openreview.net/
- Two Minute Papers: https://www.youtube.com/user/keeroyz
- ResearchGate: https://www.researchgate.net/
- Seedbank: https://tools.google.com/seedbank/
- Academic Torrents: http://academictorrents.com/
- Deep Learning Papers Reading Roadmap: https://github.com/floodsung/Deep-Learning-Papers-Reading-Roadmap
- SwiftLaTeX: https://www.swiftlatex.com/
- Lyx: https://www.lyx.org/
- Public Cloud Services Comparison - http://comparecloud.in/
- Rosetta Code - https://rosettacode.org/wiki/Category:Programming_Tasks
- Online Python Tutor - http://pythontutor.com/
- Flexbox Playground - https://demos.scotch.io/visual-guide-to-css3-flexbox-flexbox-playground/demos/
- Matrix Calculus - http://www.matrixcalculus.org/
- Metacademy - https://www.metacademy.org/
- OpenAI Gym - https://gym.openai.com/
- TensorSpace.js - https://tensorspace.org/
- Convolution Visualizer - https://ezyang.github.io/convolution-visualizer/index.html
- Picular (search engine for colors) - https://picular.co/
- Can't Unsee - https://cantunsee.space/
- Humaaans - https://www.humaaans.com/
- LanguageTool - https://languagetool.org/
- Scribens - https://www.scribens.com/
- Fast Pages (Blogging platform backed by Markdown and ipynb) - https://fastpages.fast.ai/
- Two factor auth list - https://twofactorauth.org/
- Variational Inference: A Review for Statisticians by David M. Blei, Alp Kucukelbir, and Jon D. McAuliffe http://arxiv.org/abs/1601.00670
- Texture Synthesis Using Convolutional Neural Networks http://arxiv.org/abs/1505.07376
- Jump Flooding in GPU with Applications to Voronoi Diagram and Distance Transform - http://www.comp.nus.edu.sg/~tants/jfa.html
- Understanding Neural Networks Through Deep Visualization by Jason Yosinski, Jeff Clune, Anh Nguyen, Thomas Fuchs, and Hod Lipson - http://yosinski.com/deepvis
- NIPS 2016 Tutorial: Generative Adversarial Networks by Ian Goodfellow - https://arxiv.org/pdf/1701.00160v4.pdf
- DeepMoji Demo, Paper, Blog post, FAQ
- FaceNet: A Unified Embedding for Face Recognition and Clustering - https://arxiv.org/abs/1503.03832
- Night Sight: Seeing in the Dark on Pixel Phones - https://ai.googleblog.com/2018/11/night-sight-seeing-in-dark-on-pixel.html
- BERT: State-of-the-Art Pre-training for Natural Language Processing - https://ai.googleblog.com/2018/11/open-sourcing-bert-state-of-art-pre.html
- Adobe Research Publications - http://www.adobe.com/technology/publications.html
- DeepMind Research Publications - http://deepmind.com/publications.html
- Disney Research Publications - https://www.disneyresearch.com/publications/
- Facebook Research Publications - https://research.facebook.com/publications
- Google Research Publications - http://research.google.com/pubs/papers.html
- Microsoft Research Publications - http://research.microsoft.com/apps/catalog/default.aspx?t=publications
- Pixar Online Library - http://graphics.pixar.com/library/
- Caltech - Richard Feynman, Robert Leighton, and Matthew Sands - The Feynman Lectures on Physics - http://www.feynmanlectures.caltech.edu/
- Columbia University - David Blei - Probabilistic Models for Discrete Data - F Mudd 633 - http://www.cs.columbia.edu/~blei/seminar/2016_discrete_data/index.html
- Columbia University - David Blei - Truth in Data - http://www.cs.columbia.edu/~blei/seminar/2015_truth_in_data/
- Cornell University - Spring 2015 - David Bindel - CS 4220/5223 + MATH 4260 Numerical Analysis: Linear and Nonlinear Problems - http://www.cs.cornell.edu/~bindel/class/cs4220-s15/
- NYU - Jonathan Goodman - Spring 2016 - MATH-GA 2020.001 and CSCI-GA 2421.001 - Numerical Methods II - http://www.math.nyu.edu/faculty/goodman/teaching/NumericalMethodsII2016/index.html
- Stanford University - Matthew Hoffman - Statistics 300: Advanced topics in Statistics: Bayesian nonparametrics - Summer 2013 - http://www.cs.princeton.edu/~mdhoffma/stat300/index.html
- University of Notre Dame - Nicholas Zabaras - Statistical Computing for Scientists and Engineers, Fall 2017 - https://www.zabaras.com/statisticalcomputing
- MIT 18.06 Linear Algebra - Prof. Gilbert Strang - https://www.youtube.com/watch?v=ZK3O402wf1c
- MIT 18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning - https://ocw.mit.edu/courses/mathematics/18-065-matrix-methods-in-data-analysis-signal-processing-and-machine-learning-spring-2018/video-lectures/
- Columbia University - Peter Belhumeur - Biometrics - http://www1.cs.columbia.edu/~belhumeur/courses/biometrics/2013/biometrics.html
- Columbia University - Peter Allen - Humanoid Robots - http://www1.cs.columbia.edu/~allen/S16/
- Columbia University - John Paisley - COMS W4721 Spring 2017 Machine Learning for Data Science - http://www.columbia.edu/~jwp2128/Teaching/W4721/Spring2017/W4721Spring2017.html
- NYU - Jonathan Goodman - Fall 2015 - MATH-UA.250.001 - Mathematics of Finance - http://www.math.nyu.edu/faculty/goodman/teaching/MathFin/index.html
- NYU - Jonathan Goodman - Spring 2008 - G63.2706.001 - PDE for Finance - http://www.math.nyu.edu/faculty/goodman/teaching/PDEfin/index.html
- NYU - Jonathan Goodman - All courses: http://www.math.nyu.edu/faculty/goodman/teaching/teaching.html
- Stanford University - Richard Socher - Deep Learning for NLP CS224d - http://cs224d.stanford.edu/
- Stanford University - Andrej Karpathy - Convolutional Neural Networks for Computer Vision CS231n - http://cs231n.stanford.edu/
- University College London - David Silver (One of the DeepMind guys who worked on the AlphaGo AI) - Advanced Topics: Reinforcement Learning - http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html
- University College London - Sebastian Rieder - COMP0087 Statistical Natural Language Processing - http://www.cs.ucl.ac.uk/1819/A7P/T2/COMP0087_Statistical_Natural_Language_Processing/
- University of California, Berkeley - CS188: Intro to AI, Spring 2017 - Sergey Levine - http://ai.berkeley.edu/home.html
- University of California, Berkeley - stat212b Spring 2016 - Topics Course on Deep Learning - Joan Bruna - https://github.com/joanbruna/stat212b
- University of California, Berkeley - CS 294: Deep Reinforcement Learning, Spring 2014 - Pieter Abbeel - http://ai.berkeley.edu/course_schedule.html
- University of California, Berkeley - Tutorial on Deep Learning - Ruslan Salakhutdinov - https://simons.berkeley.edu/talks/tutorial-deep-learning
- University of Oxford - Nando di Freitas - Machine Learning (includes Neural Networks in Torch, RNNs and LSTMs, Alex Graves's handwritten recognition, variational autoencoders, reinforcement learning) - https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/
- University of Toronto - Ruslan Salakhutdinov - Large Scale Machine Learning - http://www.cs.toronto.edu/~rsalakhu/STA4273_2015/
- Carnegie Mellon - Graham Neubig - Machine Translation and Sequence-to-sequence Models - http://phontron.com/class/mtandseq2seq2018/schedule.html
- Microsoft Professional Program - https://academy.microsoft.com/en-us/professional-program/tracks/
- Google Machine Learning Crash Course - https://developers.google.com/machine-learning/crash-course/
- CS 20: Tensorflow for Deep Learning Research - taught by Chip Huyen - http://web.stanford.edu/class/cs20si/
- Columbia University - Julia Hirschberg and Sarah Ita Levitan - COMS 6998: Advanced Topics in Spoken Language Processing - http://www.cs.columbia.edu/~julia/courses/CS6998-2019/syllabus19.html
- Columbia University - Peter Belhumeur - Computational Photography Spring 2010 - http://www1.cs.columbia.edu/~belhumeur/courses/compPhoto/compPhoto.html
- Columbia University - Shree Nayar - Computational Imaging
- Cornell University - Steve Marschner - CS6630 Realistic Image Synthesis
- Fall 2015: http://www.cs.cornell.edu/Courses/cs6630/2015fa/
- Spring 2012: http://www.cs.cornell.edu/Courses/cs6630/2012sp/
- MIT - Ramesh Raskar - Fall 2009 - MAS.531 / MAS.131 - Computational Camera and Photography - http://ocw.mit.edu/courses/media-arts-and-sciences/mas-531-computational-camera-and-photography-fall-2009/
- UPenn - CIS 700 - Special Topics in Procedural Graphics - https://cis700-procedural-graphics.github.io/
- University of Pennsilvania (Wharton School of Business) - Business Foundations Specialization - https://www.coursera.org/specializations/wharton-business-foundations
- CMU - Advanced Cloud Computing - http://www.cs.cmu.edu/~15719/
- Stanford University - Mark Levoy - CS 178 - Digital Photography - http://graphics.stanford.edu/courses/cs178/
- Google - Mark Levoy - Lectures on Digital Photography - https://www.youtube.com/watch?v=y7HrM-fk_Rc&list=PL7ddpXYvFXspUN0N-gObF1GXoCA-DA-7i
- Databricks Academy (Note: free for Microsoft employees) - https://academy.databricks.com/
- MIT - The Missing Semester of Your CS Education - https://missing.csail.mit.edu/
- Columbia University - Tony Jebara - Machine Learning 4771 - http://www.cs.columbia.edu/~jebara/4771/
- Columbia University - Tony Jebara - Adv. Machine Learning 4771 - http://www.cs.columbia.edu/~jebara/4772/
- Columbia University - Michael Collins - Natural Language Processing 4705 - http://www.cs.columbia.edu/~cs4705/
- Columbia University - John Paisley - Bayesian Models for Machine Learning: - http://www.columbia.edu/~jwp2128/Teaching/E6892/E6892Fall2015.html
- Columbia University - David Blei - Foundations of Graphical Models - http://www.cs.columbia.edu/~blei/fogm/2015F/index.html http://www.cs.columbia.edu/~blei/fogm/2016F/index.html
- How to compose an NSF GRFP application packet you can be proud of - by Katie Kuksenok - https://medium.com/ok-work/how-to-compose-an-nsf-grfp-application-packet-you-can-be-proud-of-b5f848dee19
- Some tips for your Google Summer of Code Application - by Chris Rackauckas - http://www.stochasticlifestyle.com/tips-google-summer-code-application/
- A Survival Guide to a PhD - by Andrej Karpathy - http://karpathy.github.io/2016/09/07/phd/
- Resume for Machine Learning by Siraj Raval - https://www.youtube.com/watch?v=nMK94JlKRb4
- Getting What You Came For: The Smart Student's Guide to Earning an M.A. or a Ph.D. by Robert Peters - https://www.amazon.com/Getting-What-You-Came-Students/dp/0374524777
- How To Get Hired -- What CS Students Need to Know. By Dan Kegel - http://www.kegel.com/academy/getting-hired.html
- FAQ: Preparing for a Job in Data Science, By Chris Wiggins - https://gist.github.com/chrishwiggins/030a3b8b0c8e6861d450
- The Guerilla Guide to Interviewing, by Joel Spolsky - https://www.joelonsoftware.com/2006/10/25/the-guerrilla-guide-to-interviewing-version-30/
- Writing a Google AI Residency Cover Letter by Katherine Lee and Ben Eysenbach - https://colinraffel.com/blog/writing-a-google-ai-residency-cover-letter.html
- Statement of Purpose Examples from Ph.D. Program Applications by Philip Guo - http://www.pgbovine.net/PhD-application-essay-examples.htm
- Machine Learning PhD Applications — Everything You Need to Know - by Tim Dettmers - http://timdettmers.com/2018/11/26/phd-applications/
- ML Statement of Purpose examples (Google search) - https://www.google.com/search?q=machine+learning+phd+statement+of+purpose+filetype%3Apdf
- Four lessons I learned after my first full-time job after college - by Chip Nguyen - https://huyenchip.com/2019/12/23/leaving-nvidia-lessons.html
- Doing well in your courses - by Andrej Karpathy - https://cs.stanford.edu/people/karpathy/advice.html
- A survivor’s guide to Artificial Intelligence courses at Stanford by Chip Huyen - https://huyenchip.com/2018/03/30/guide-to-Artificial-Intelligence-Stanford.html
- Twenty things I wish I’d known when I started my PhD by Lucy Taylor - https://www.nature.com/articles/d41586-018-07332-x
- Heuristics for Scientific Writing (a Machine Learning Perspective) - by Zachary C. Lipton - http://approximatelycorrect.com/2018/01/29/heuristics-technical-scientific-writing-machine-learning-perspective/
- How to Get Your SIGGRAPH Paper Rejected, by Jim Kajiya - https://www.siggraph.org/sites/default/files/kajiya.pdf
- You and Your Research - by Richard Hamming - Video | Transcription
- Principles of Effective Research - by Michael Nielsen - http://michaelnielsen.org/blog/principles-of-effective-research/
- An Opinionated Guide to ML Research - by John Schulman - http://joschu.net/blog/opinionated-guide-ml-research.html
- Questions to Ask a Prospective Ph.D. Advisor on Visit Day, With Thorough and Forthright Explanations - by Andrew Kuznetsov - https://blog.ml.cmu.edu/2020/03/02/questions-to-ask-a-prospective-ph-d-advisor-on-visit-day-with-thorough-and-forthright-explanations/
- Teach Yourself Programming in Ten Years - by Peter Norvig - http://norvig.com/21-days.html
- Tool Spotlight: Apps for Writing - by Katie Kuksenok - https://medium.com/ok-work/apps-i-m-using-for-writing-a-lot-7945ee8737fd
- Getting things done - by Julia Evans - https://jvns.ca/blog/2016/09/19/getting-things-done/
- So you want to be a wizard - by Julia Evans - https://jvns.ca/blog/so-you-want-to-be-a-wizard/ - https://jvns.ca/zines/
- STAR (situation, task, action, result) - Wiki link
- SMART (Specific, Measurable, Achievable, Results-Based, Time-bound) - https://en.wikipedia.org/wiki/SMART_criteria
- On Avoiding Stress Culture - by Jean Yang - http://jxyzabc.blogspot.com/2016/09/on-avoiding-stress-culture.html
- The Genius Fallacy - by Jean Yang - http://jxyzabc.blogspot.com/2017/09/the-genius-fallacy.html
- The Feynman Technique: The Best Way to Learn Anything (article by Farnam Street) - https://www.fs.blog/2012/04/learn-anything-faster-with-the-feynman-technique/
- Calendar. Not to-do lists. by Devi Parikh - https://medium.com/@deviparikh/calendar-in-stead-of-to-do-lists-9ada86a512dd
- 5 Ways to Increase Productivity by Siraj Raval - https://www.youtube.com/watch?v=N2_MtrTn1hE&feature=youtu.be
- Effectiveness and Efficiency by Daniel Moth - http://www.danielmoth.com/Blog/Effectiveness-And-Efficiency.aspx
- Attention is your scarcest resource by Ben Kuhn - https://www.benkuhn.net/attention/
- GOTO 2012 • Scaling Yourself • Scott Hanselman - https://m.youtube.com/watch?v=FS1mnISoG7U
- Pixar’s 22 Rules of Storytelling - by Emma Coats - https://www.aerogrammestudio.com/2013/03/07/pixars-22-rules-of-storytelling/
- Rules of Machine Learning: Best Practices for ML Engineering - by Martin Zinkevich - http://martin.zinkevich.org/rules_of_ml/rules_of_ml.pdf
- Should I do the thing? by Kit Kuksenok - https://ksen0.github.io/do-the-thing/
- Meetup Cookbook - https://tigrennatenn.neocities.org/meetup_cookbook.html
- Lessons from my first year of live coding on Twitch by Suz Hinton - https://medium.freecodecamp.org/lessons-from-my-first-year-of-live-coding-on-twitch-41a32e2f41c1
- Notes to myself on software engineering by François Chollet - https://medium.com/@francois.chollet/notes-to-myself-on-software-engineering-c890f16f4e4d
- Some Possible Career Goals by Julia Evans - https://jvns.ca/blog/2018/09/30/some-possible-career-goals/
- Career advice for recent Computer Science graduates by Chip Huyen - https://huyenchip.com/2018/10/08/career-advice-recent-cs-graduates.html
- 7 absolute truths I unlearned as junior developer - https://monicalent.com/blog/2019/06/03/absolute-truths-unlearned-as-junior-developer/
- Personal Finance Wiki (from Reddit's r/personalfinance) - https://www.reddit.com/r/personalfinance/wiki/index
- Do I Need to Go to University? - https://colah.github.io/posts/2020-05-University/
- Advice for Better Blog Posts by Rachel Thomas - https://www.fast.ai/2019/05/13/blogging-advice/