Comprehensive guides, from-scratch implementations, curated paper collections, and hands-on tutorials across LLMs, diffusion models, computer vision, and ML systems.
From fundamentals to scaling laws — everything about large language models
A comprehensive, chapter-by-chapter guide to LLMs — from probability basics to scaling laws, with hands-on fine-tuning code and exercises.
PyQt5 desktop GUI for fine-tuning, evaluating, and deploying LLMs using torchtune — no command-line required. Full visual workflow.
Comprehensive guides for working with Large Language Models — architectures, training pipelines, and deployment strategies explained in depth.
Low-Rank Adaptation of Large Language Models — parameter-efficient fine-tuning implemented from scratch with detailed code walkthrough.
KV cache optimization, model quantization, and LLM compression research
Curated collection of 150+ research papers on KV Cache Management, KV Cache Compression, and LLM Compression for efficient inference.
Curated list of papers, docs, and code about model quantization — aimed at providing comprehensive info for quantization research.
Hands-on tutorial combining YOLOX with Quantization Aware Training and Knowledge Distillation for efficient real-time object detection.
A comprehensive, book-style tutorial covering everything about ONNX — from fundamentals to production deployment and optimization.
Image generation, text diffusion, and denoising from theory to implementation
Text generation using diffusion-style denoising — iteratively refining noisy sequences into coherent text, an alternative to autoregressive LLMs.
Implementation of diffusion-based denoising with reparameterization — organized and shared with detailed code walkthrough.
Comprehensive survey and taxonomy of diffusion model papers — organized by architecture, application, and training methodology.
Efficient attention mechanisms, linear attention, and transformer architecture surveys
Three in-depth surveys covering efficient transformer architectures, attention variants, and optimization techniques for scalable inference.
Refining Gated Linear Attention — efficient alternative to softmax attention for scalable sequence modeling with linear complexity.
Understanding dimensionality reduction from classical PCA through autoencoders to Variational Autoencoders — theory and implementation.
Image enhancement, object detection, segmentation, and low-level vision
Super-Resolution, denoising, deblurring, dehazing, low-light enhancement, artifact removal — end-to-end models with PSNR/SSIM benchmarks.
Complete training system with PyQt5 desktop app — LoRA/QLoRA fine-tuning, knowledge distillation, ONNX export, INT8 quantization. 100+ FPS.
Modular PyTorch implementation of YOLOv8 for object detection with clear architecture, training pipelines, and detailed documentation.
From-scratch implementations of classic feature detection and description algorithms — SIFT, SURF, ORB, Harris, and more.
Remove objects from photos including shadows and reflections using generative inpainting — end-to-end diffusion-based restoration.
Low-light image enhancement directly on Bayer pattern data — RAW image processing with deep learning for mobile camera pipelines.
System design, data drift, monitoring, and production ML architecture
Comprehensive guide to ML System Design — LLM serving, training pipelines, scaling, and real-world architecture patterns for interviews and practice.
Research on data drift — taxonomy (covariate/concept/label shift), mathematical formulations (KL, PSI, Wasserstein), monitoring architectures, and 200+ curated papers.
ML research foundations, linear algebra, DSA, and courses
Structured, end-to-end roadmap for becoming a strong ML researcher — mathematical foundations, ML theory, deep learning, optimization, and efficient ML.
Well-organized repository covering core DSA from fundamentals to advanced topics — systematic learning, coding interviews, and reference implementations.
Curated computer vision interview questions covering CNNs, object detection, segmentation, GANs, transformers, and production deployment.
Awesome lists, paper collections, and survey compilations
Curated record of papers on low-level vision tasks — super-resolution, denoising, deblurring, and image restoration research.
Curated list of research papers on efficient attention mechanisms and transformer architectures for NLP and vision.
Curated collection of Vision-Language Model architectures — from CLIP to GPT-4V, covering multi-modal learning and alignment.
Collection of OCR research papers — scene text detection, recognition, and end-to-end document understanding systems.