Artem Ryblov’s Data Science Weekly

A/B Testing & Experimentation Roadmap This roadmap is for analysts, data scientists, and product folks who want to go from “I know what an A/B test is” to running trustworthy, advanced online experiments (CUPED, sequential testing, quasi-experiments, Bayesian, etc.). It’s organized by topics. You don’t have to go strictly top-to-bottom, but earlier sections are foundations for later ones. Link: GitHub Navigational hashtags: #armknowledgesharing #armtutorials General hashtags: #statistics #abtest...

Deep Learning Tuning Playbook by Google This document helps you train deep learning models more effectively. Although this document emphasizes hyperparameter tuning, it also touches on other aspects of deep learning training, such as training pipeline implementation and optimization. This document assumes your machine learning task is either a supervised learning problem or a similar problem (for example, self-supervised learning) That said, some of the advice in this document may also apply to ...

A/B Testing course by Skoltech Each of us regularly makes decisions. The optimal solution is often not obvious, and the cost of error is high. A/B tests are the most accurate way to choose the best option. A/B experiments are used to test the effectiveness of new drugs and are also widely used in business. Companies that use A/B experiments make more accurate decisions, allowing them to stay ahead of the competition. Mathematical statistics is the foundation of A/B tests. It provides mathematica...

PyTorch internals This talk is for those of you who have used PyTorch, and thought to yourself, "It would be great if I could contribute to PyTorch," but were scared by PyTorch's behemoth of a C++ codebase. I'm not going to lie: the PyTorch codebase can be a bit overwhelming at times. The purpose of this talk is to put a map in your hands: to tell you about the basic conceptual structure of a "tensor library that supports automatic differentiation", and give you some tools and tricks for finding...

Machine Learning Q and AI. 30 Essential Questions and Answers on Machine Learning and AI by Sebastian Raschka If you’re ready to venture beyond introductory concepts and dig deeper into machine learning, deep learning, and AI, the question-and-answer format of Machine Learning Q and AI will make things fast and easy for you, without a lot of mucking about. Born out of questions often fielded by author Sebastian Raschka, the direct, no-nonsense approach of this book makes advanced topics more acc...

Tech Interview Cheat Sheet This list is meant to be both a quick guide and reference for further research into these topics. It's basically a summary of that comp sci course you never took or forgot about, so there's no way it can cover everything in depth. Link: Site Navigational hashtags: #armknowledgesharing #armtutorials General hashtags: #interview #techinterview #interviewprep #interviewpreparation @data_science_weekly

Machine Learning Design Primer Some helpful notes for Machine Learning System Design Interview preparation, which author gathered from various resources to prepare for machine learning systems design interview. Link: GitHub Navigational hashtags: #armknowledgesharing #armtutorials General hashtags: #interview #techinterview #interviewprep #interviewpreparation #mlsd #mlsystemdesign #mlsysdes #systemdesign @data_science_weekly

System Design 101 Explain complex systems using visuals and simple terms. Whether you're preparing for a System Design Interview or you simply want to understand how systems work beneath the surface, we hope this repository will help you achieve that. Link: Repo Navigational hashtags: #armknowledgesharing #armrepo General hashtags: #systemdesign @data_science_weekly

Summary of the year for the channel "Artem Ryblov’s Data Science Weekly" from @TGStat

Build a Large Language Model by Sebastian Raschka In Build a Large Language Model (from Scratch) bestselling author Sebastian Raschka guides you step by step through creating your own LLM. Each stage is explained with clear text, diagrams, and examples. You’ll go from the initial design and creation, to pretraining on a general corpus, and on to fine-tuning for specific tasks. Build a Large Language Model (from Scratch) teaches you how to: • Plan and code all the parts of an LLM • Prepare a data...