# Alex Dremov
> I'm a Machine Learning Researcher and Engineer. Here I write posts on the intersection of deep learning theory and efficient machine learning
Public Ghost content for AI and LLM tooling. Use `/llms-full.txt` for consolidated page and post context.
Append `.md` to any post or page URL to get the content in Markdown (for example, `/example-post.md`).

## Pages
- [Hey, I'm Alex 👋](https://alexdremov.me/about.md) - I'm an AI researcher and ML engineer. I hold an M.Sc. in Data Science from EPFL and a B.Sc. in CS from MIPT. Over the last couple of years, I've built real-time speech recognition systems from the ground up at Yandex and researched compute-optimal quantization at Apple. My academic work on efficien…
- [PRIVACY NOTICE](https://alexdremov.me/privacy-policy.md) - This privacy notice for Aleksandr Dremov's Blog

## Posts
- [Managing Thousands of DL Experiments and Staying Sane](https://alexdremov.me/managing-thousands-of-dl-experiments-and-staying-sane.md) - At scale, launching ML research experiments is already complicated. Analyzing them without proper planning is just impossible. Here I describe tips about how I do ML experiments
- [Rethinking Quantization-Aware Training: Why Your QAT Length is Probably Wrong](https://alexdremov.me/rethinking-quantization-aware-training-why-your-qat-length-is-probably-wrong.md) - Training quantized neural networks involves a fundamental trade-off: how should you divide your compute budget between full-precision pretraining and quantization-aware training?
- [Understanding Flash Attention: Writing the Algorithm from Scratch in Triton](https://alexdremov.me/understanding-flash-attention-writing-the-algorithm-from-scratch-in-triton.md) - Why is Flash Attention so fast? Find out how Flash Attention works. Afterward, we'll polish our understanding by writing a GPU kernel of the algorithm in Triton.
- [Speed Up PyTorch With Custom Kernels. But It Gets Progressively Darker](https://alexdremov.me/speed-up-pytorch-with-custom-kernels-but-it-gets-progressively-darker.md) - It's all about making your models run faster, from flicking a magic “compile” switch to writing your own custom GPU code. In each step, we’ll implement an innocent softmax function, but things are about to get dark by the end.
- [Simple Ways to Speed Up Your PyTorch Model Training](https://alexdremov.me/simple-ways-to-speedup-your-pytorch-model-training.md) - If all machine learning engineers want one thing, it's faster model training — maybe after good test metrics.
- [Swift Actors — Common Problems and Tips](https://alexdremov.me/swift-actors-common-problems-and-tips.md) - Swift actors are a powerful tool to address data races and make your code thread-safe. However, it is also quite a sophisticated concept that requires deep understanding to write efficient and bug-free code.
- [I Contributed to PyTorch. Here's What I Learned](https://alexdremov.me/i-contributed-to-pytorch-heres-what-i-learned.md) - When you see something that does not work in an omnipresent framework, you believe it can't be completely broken, right?
- [Conquer Data Races with Swift Actors](https://alexdremov.me/conquer-data-races-with-swift-actors.md) - Unleash the power of Swift concurrency with Actors! Get all the information you need in this comprehensive article
- [Dive into Swift's Memory Management](https://alexdremov.me/dive-into-swifts-memory-management.md) - Swift uses ARC to track and deallocate unused objects. Learn about the three types of reference counts and how ARC works — in this detailed post.
- [Data Binding in SwiftUI: Tips, Tricks, and Best Practices](https://alexdremov.me/data-binding-in-swiftui-tips-tricks-and-best-practices.md) - Want to create dynamic and responsive user interfaces in SwiftUI? Data binding is the key! In this tutorial, I'll show you how to use @State, @ObservedObject, @EnvironmentObject, and @Binding to keep your user interface in sync with your data
- [iOS App As a Microservice. Using SwiftUI in Modular App](https://alexdremov.me/ios-app-as-a-microservice-using-swiftui-in-modular-app.md) - The modular architecture is excellent. But how to implement it effectively with SwiftUI? From its core, SwiftUI is state-driven, and it can be tricky to modularize an app and define exact responsibility borders.
- [iOS App As a Microservice. Modularize Your App With Tuist](https://alexdremov.me/ios-app-as-a-microservice-modularize-your-app-with-tuist.md) - This is the second article in a series on modular app architecture. In this post, I will cover implementation details using Tuist
- [iOS App As a Microservice. Build Robust App Architecture](https://alexdremov.me/ios-app-as-a-microservice-build-robust-app-architecture.md) - What will you choose: MVVM, MVC, VIPER? Those all are local and problem-specific architectures. But how to structure your app on a larger scale to make it scalable and well-organized?
- [Exploring SwiftUI Layout Protocol | Creating Custom Layout](https://alexdremov.me/exploring-swiftui-layout-protocol-creating-custom-layout.md) - Apple introduces new SwiftUI's Layout protocol with the release of iOS 16. It is a powerful tool for constructing custom views with SwiftUI elegance.
- [SwiftUI Navigation Is a Mess. Here’s What You Can Do](https://alexdremov.me/swiftui-navigation-is-a-mess-heres-what-you-can-do.md) - Managing navigation in pure SwiftUI is hard and leads to messy solutions. In this post, I will show you how you can manage views effectively
- [Suffix Automaton and Rickroll Lyrics Graph](https://alexdremov.me/suffix-automaton-and-rickroll.md) - Easy to understand explanation of suffix automaton with implementation. Finally, generating correct Rickroll lyrics suffix automaton
- [Using Threads in Swift](https://alexdremov.me/using-threads-in-swift.md) - Swift provides DispatchQueue as an excellent layer above raw threads. But sometimes you want to use a low-level thread API
- [SwiftUI Advanced Animation: Morphing Shapes](https://alexdremov.me/swiftui-advanced-animation.md) - I'm going to show how complex SwiftUI views can be animated efficiently using VectorArithmetic protocol with Accelerate library for fast computations.
- [New Package: Look at Swift Async Algorithms](https://alexdremov.me/swift-async-algorithms-module.md) - Apple released the first version of the async swift algorithms package. It provides tools and algorithms to use with the introduced not that far ago Async Sequence
- [Treap: The Easiest Search Tree (Explained)](https://alexdremov.me/treap-algorithm-explained.md) - Binary search trees are mostly hard. Writing red-black tree is a nightmare. Here, I'm going to explain one of the easiest, yet efficient and powerful balanced binary tree — treap or cartesian tree
- [Type Placeholders: New Swift 5.6 Feature](https://alexdremov.me/swift-type-placeholders.md) - Type placeholders were recently introduced in Swift 5.6. Get in touch with new useful Swift feature.
- [Quick Guide to Async Await in Swift](https://alexdremov.me/quick-guide-to-async-await-in-swift.md) - Everything you need to know about new Swift asynchronous features. Async await, main actor, task, async get, and possible use cases — all covered.
- [Top 7 Subtle Swift Features](https://alexdremov.me/top-7-subtle-swift-features.md) - Here, I collected Swift features that are less known and can be useful when you prepare for interviews or want to deepen your Swift knowledge.
- [Note-taking apps](https://alexdremov.me/note-taking-apps.md) - Here I cover note-taking apps for productivuty and creating your own knowledge database
- [The Mystery of Mach-O Object Structure](https://alexdremov.me/mystery-of-mach-o-object-file-builders.md) - I’m going to tell you about the internals of the Mach-O file and give an introduction to the simple relocatable object file structure
- [Skip List Indexation and kth Maximum](https://alexdremov.me/skip-list-indexation-and-kth-maximum.md) - Skip List is a nice structure that lets you to perform insertions, searches, and finding n-th maximum. In this post I focus on skip list indexation
- [How Deep Neural Networks Work](https://alexdremov.me/how-deep-neural-networks-train.md) - Here, I combine the explanation of Neural Nets with coding. By the end, we will develop a basic neural network and try to solve usual problems

## Optional
- [RSS Feed](https://alexdremov.me/rss/)
- [Sitemap](https://alexdremov.me/sitemap.xml)
- [Full content of pages and posts](https://alexdremov.me/llms-full.txt)