Found 1 results for "tag:interesting_blogs"

• Links • Max Halford

  https://maxhalford.github.io/links/

  Smart people Tim Salimans on Data Analysis Randal Olson Sam & Max – French and NSFW! Sebastian Raschka Clean Coder Pythonic Perambulations Erik Bernhardsson otoro Terra Incognita Real Python Airbnb Engineering No Free Hunch The Unofficial Google Data Science Blog will wolf Edwin Chen Use the index, Luke! Jack Preston Agustinus Kristiadi DataGenetics Katherine Bailey Netflix Research inFERENce Hyndsight – Rob Hyndman is a time series specialist. While My MCMC Gently Samples Ines Montani – by one of the founders of spaCy. Stephen Smerity Peter Norvig IT Best Kept Secret Is Optimization – By Jean-Francois Puget, aka CPMP. explained.ai Better Explained Genetic Argonaut pandas blog Towards Data Science Probably Overthinking It Simply Statistics Practically Predictable koaning – by Vincent Warmerdam, who made calmcode blogarithms Possibly Wrong FastML Parameter-free Learning and Optimization Algorithms Todd W. Schneider – This guy is really good at exploratory data analysis. Yann Thaddée – Not directly related to data science but interesting nonetheless. Colins Blog Fabien Sanglard – nothing to do with data science, but such good taste! The Glowing Python – By the creator of MiniSom, which is worth checking out too. Matt Hancock Francis Bach – Someone with an h-index of 80+ who takes the time to blog is worth reading. Gwern Branwen – Cool in a weird way. Libres pensées d’un mathématicien ordinaire Count Bayesie Jim Savage Nick Higham – A lot of well explained algebra. Calmcode – Not a blog per se, but a nice collection of short to the point tutorials about various tools. Chris Said Evan Miller Eric Jang Andrey Akinshin Single Lunch Freakonometrics Martin Daniel Chris Kiehl ithaka.im – A guy I met who travelled for 6 years with his wife on a bike, very inspiring. Muthukrishnan – Has written some neat document processing stuff. Björn Ottosson Guilherme Duarte Marmerola Cal Paterson Claire Carroll Luke Metz – Luke is working on the niche topic of meta-learning at Google. He also happens to a very kind person. Practical Recommendations – A blog about recommender systems. Robin Linacre – Some good stuff related to record linkage. Neal Lathia – Machine learning in production stuff. John D. Cook Brandon Roberts Allen Downey Christophe Blefari Scott Rome Eugene Yan Lj Miranda death and gravity – Great advanced Python resource. The Shape of Data IDEA Shaded relief Leslie Lamport Curtis Miller Naftali Harris Laird Breyer – wrote some cool software for text classification called dbacl, and markovpr which is a PageRank implementation. Vicky Boykis – the OG behind Normconf Danielle Navarro Amit Patel – visual explanations of algorithms used in games. nogilnick Nat Bullard – known for making annual presentations on the state of decarbonization. Max Woolf Yuin Chien – does design at Google. Matt Webb – this madlad has been blogging since 2000. Caleb Gross Geoffrey Litt Clayton Ramsey Rusty Conover Austin Z. Henley Charlie Marsh Andy Pavlo – reviews database landscape every year. Machine learning The Elements of Statistical Learning - Jerome H. Friedman, Robert Tibshirani, and Trevor Hastie Machine Learning - Tom Mitchell – I think this wonderful textbook is under-appreciated. Artificial Intelligence: A Modern Approach - Russel & Norvig mlcourse.ai – Of all the introductions to machine learning, I think this is the one that strikes the best balance between theory and practice. Machine learning cheat sheets - Shervine Amidi Kalman and Bayesian Filters in Python - Roger Labbe – Kalman filters are notoriously hard to grok, this tutorial nicely builds up the steps to understanding them. CS231n Convolutional Neural Networks for Visual Recognition - Stanford Algorithmes d’optimisation non-linéaire sans contrainte (French) - Michel Bergmann Graphical Models in a Nutshell - Koller et al. Rules of Machine Learning: Best Practices for ML Engineering - Martin Zinkevich – You should read this once a year. A Few Useful Things to Know about Machine Learning - Pedro Domingos – This short paper summarizes basic truths in machine learning. How to Write a Spelling Corrector - Peter Norvig – Magic in 36 lines of code. MCMC sampling for dummies - Thomas Wiecki Your Easy Guide to Latent Dirichlet Allocation An Intuitive Explanation of Convolutional Neural Networks - Ujjwal Karn An overview of gradient descent optimization algorithms - Sebastian Ruder How to explain gradient boosting - Terence Parr and Jeremy Howard – A very good introduction to vanilla gradient boosting with step by step examples. Why Does XGBoost Win “Every” Machine Learning Competition? - Didrik Nielsen – This Master’s thesis goes into some of the details of XGBoost without being too bloated. Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations The Cramér-Rao Lower Bound on Variance: Adam and Eve’s “Uncertainty Principle” - Michael Powers A Concrete Introduction to Probability (using Python) - Peter Norvig – Extremely elegant Python coding. The Hungarian Maximum Likelihood Trick - Louis Abraham Machine Learning for Signal Processing - University of Illinois Gaussian Process, not quite for dummies - Yuge Shi – Gaussian processes are quite difficult to understand (at least, for me) but Yuge gives some great visual intuitions. Frequentism and Bayesianism: A Python-driven Primer - Jake VanderPlas Variational Inference: A Review for Statisticians - David Blei and his flock The Performance of Decision Tree Evaluation Strategies - Andrew Tulloch Simplifying Graph Convolutional Networks - Felix Wu et al. – A nice example of putting the horse before the cart. MIT 6.867 machine learning course notes - Tommi Jaakola – For people who enjoy concise mathematical notation. A Recipe for Training Neural Networks - Andrej Karpathy The Bitter Lesson - Richard Sutton Introduction to Locality-Sensitive Hashing - Tyler Neylon Transformers from scratch - Peter Bloem A Machine Learning Primer - Mihail Eric – A good read for beginners in machine learning algorithms. Fitting Bayesian structural time series with the bsts R package - Steven L. Scott Super Fast String Matching in Python - Chris van den Berg Poisson regression and non-normal loss - scikit-learn Perfect lung cancer detections in a $1 million ML competition with an ingenious hack - Yusaku Sako Beware Default Random Forest Importances From RankNet to LambdaRank to LambdaMART: An Overview Word2Vec Tutorial - The Skip-Gram Model - Chris McCormick Causal Inference for The Brave and True Data Distribution Shifts and Monitoring - Chip Huyen Creating Confidence Intervals for Machine Learning Classifiers - Sebastian Raschka Machine Learning @ VU SVD Image Compression, Explained - Dennis Miczek Classifying all of the pdfs on the internet - Santiago Pedroza Weird Kaggle, the superiority of books, and other reflections - Nick Griffiths Transformers from scratch - Peter Bloem Explaining RNNs without neural networks Practical Deep Learning for Coders What Is ChatGPT Doing… and Why Does It Work? CARTE: toward table foundation models - Gaël Varoquaux Everything you always wanted to know about extreme classification - Microsoft Research – I love the idea that recsys can be framed as an extreme classification problem. Line scan camera image processing - Daniel Lawrence Lu Data science Make for data scientists - Paul Butler Tidy Data - Hadley Wickham – You need to be aware of this framework if you want to be serious about analysing tabular data. Modeling marketing attribution - Claire Carroll – I worked on this problem for a short time at Alan. I definitely would have done a better job if I had read this article first. Darts, Dice, and Coins: Sampling from a Discrete Distribution - Keith Schwarz Unprojecting text with ellipses - Matt Zucker – See also this article on page dewarping by the same author. Language models, classification and dbacl - Laird A. Breyer – Machine learning on text with a UNIX philosophy. Teaching An Old Dog A New Trick - Chris Kamphuis Optimal Peanut Butter and Banana Sandwiches - Ethan Rosenthal The Data Science Hierarchy of Needs - Monica Rogati Tuesday Changes Everything - Jesper Juul Doing Named Entity Recognition? Don’t optimize for F1 - Christopher Manning – A rather niche topic, but well explained. Lessons learned building an ML trading system that turned \$5k into \$200k Common statistical tests are linear models (or: how to teach stats) - Jonas Kristoffer Lindeløv Kelly Can’t Fail - John Mount Analytics engineering Input metrics & weekly business review - Working backwards If spreadsheets are eternal, are BI tools transitory? Dimensional Modeling Techniques – old but gold. Motif – they had cool ideas about funnel analytics before they sadly shut down. Facets – it’s too bad this project is not actively maintained anymore, it had a lot of potential. Crossfilter – I like these tools that let you slice and dice data in the browser. Educational material Paul’s Online Notes Mathematics Quizzes Simulating Fluids, Fire, and Smoke in Real-Time - Andrew Chan GPS - Bartosz Ciechanowski – all his articles are great. Grid.Space Data engineering Emerging Architectures for Modern Data Infrastructure What your data team is using: the analytics stack - Technically – Another solid article to understand what an analytics stack looks like in 2021. Multiworld Testing Decision Service: A System for Experimentation, Learning, And Decision-Making Machine Learning: The High-Interest Credit Card of Technical Debt - Google Continuous Delivery for Machine Learning - Martin Fowler Hidden Technical Debt in Machine Learning Systems - Google The Log: What every software engineer should know about real-time data’s unifying abstraction - Jay Kreps Command-line Tools can be 235x Faster than your Hadoop Cluster - Adam Drake Git scraping, the five minute lightning talk - Simon Willison – I wish I had thought about this first! Gently down the stream - Mitch Seymour Turning the database inside-out with Apache Samza The Snowflake Elas

  interesting_blogs