mark-selyaeff
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Theory of digital signal processing (DSP): signals, filtration (IIR, FIR, CIC, MAF), transforms (FFT, DFT, Hilbert, Z-transform) etc.
D-FINE: Redefine Regression Task of DETRs as Fine-grained Distribution Refinement [ICLR 2025 Spotlight]
A reactive notebook for Python — run reproducible experiments, query with SQL, execute as a script, deploy as an app, and version with git. All in a modern, AI-native editor.
Create your custom OpenCV algorithms using a user-friendly node editor interface, inspired by Blender and Unreal Engine blueprints! Quickly prototype your vision using live previews as you edit.
Video+code lecture on building nanoGPT from scratch
convert mmdetection model to tensorrt, support fp16, int8, batch input, dynamic shape etc.
Re-imagining Windows like a minimal OS install, already debloated with minimal impact for most functionality.
A library for experimenting with, training and evaluating neural networks, with a focus on adversarial robustness.
An opinionated list of awesome Python frameworks, libraries, software and resources.
Working files for the textbook project "Machine Learning. The Basics"
More routines for operating on iterables, beyond itertools
Toolbox of models, callbacks, and datasets for AI/ML researchers.
My best practice of training large dataset using PyTorch.
A jupyter notebook having all most frequent used code snippet for daily data scienceoperations
A python library for self-supervised learning on images.
PyTorch Tutorial for Deep Learning Researchers
Ready-to-run Docker images containing Jupyter applications
🤖 Python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained
Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech!
Format and convert Python docstrings and generates patches
A re-implementation of mtcnn. Joint training, tutorial and deployment together.
A complete computer science study plan to become a software engineer.