Tag: #ai-papers
Writing on GPUs, LLMs, MLOps, Kubernetes — and mindset · 79 posts
RWKV: Reinventing RNNs for the Transformer Era — From v4 to v7 Goose
Analyzing the RWKV architecture that overcomes the O(N²) limitation of Transformers. We explore the fusion of Linear Attention and RNNs, selective state space mechanisms, and the innovations in v7 Goose — all with code e
2026-03-03 · 9 min read #ai-papers#rwkv#rnn#linear-attention#state-space-modelRWKV-7 "Goose" Architecture Analysis — A Linear-Time Model Surpassing Transformers
A paper-based analysis of RWKV-7 Goose Dynamic State Evolution mechanism, TC0 barrier breakthrough, and performance comparison against Transformers. A next-generation architecture enabling constant memory + linear time i
2026-03-03 · 9 min read #ai-papers#rwkv#linear-attention#sequence-modeling#2026-03Mixture of Experts (MoE) Architecture: A Complete Analysis
A complete analysis of MoE architectures, from the principles of Sparse MoE to the MoE implementations in Mixtral and DeepSeek-V3, routing strategies, and load balancing.
2026-03-03 · 6 min read #ai-papers#moe#mixtral#deepseek#2026-03Diffusion Transformer (DiT) Architecture Analysis: The Shift from U-Net to Transformer
An analysis of the Scalable Diffusion Models with Transformers (DiT) paper. We cover the motivations behind transitioning from U-Net backbones to Transformers, adaLN-Zero conditioning, scaling laws, and the downstream im
2026-03-03 · 10 min read #ai-papers#diffusion-transformer#dit#generative-ai#image-generationNeMo Guardrails Complete Guide: Building Programmable Safety Controls for LLM Applications
A hands-on guide to building programmable safety controls for LLM-based applications using NVIDIA NeMo Guardrails, covering input/output moderation, topic control, and hallucination detection.
2026-03-03 · 23 min read #ai-papers#nemo-guardrails#llm-safety#nvidia#guardrailsMamba: Linear-Time Sequence Modeling with Selective State Spaces — Paper Analysis
A deep dive into the Mamba architecture, a promising alternative to Transformers. We explore the core idea behind Selective State Space Models, the hardware-aware algorithm, and experimental results along with code.
2026-03-02 · 7 min read #ai-papers#mamba#ssm#state-space-model#transformerReinforcement Learning Complete Guide: From Theory to the Latest Algorithms and Real Implementations
Derives the major algorithms with their mathematics — from the core theory of MDPs and the Bellman equations through Q-Learning, DQN, Policy Gradient, A3C, PPO and SAC — and pulls together RLHF, AlphaGo and robotics appl
2026-03-01 · 46 min read #reinforcement-learning#deep-learning#dqn#ppo#rlhfComplete Analysis of the GPT Series Papers: The Journey from GPT-1 to GPT-4, How Language Models Changed the World
A complete generation-by-generation analysis of OpenAI GPT series. From GPT-1 unsupervised pre-training, GPT-2 zero-shot learning, GPT-3 in-context learning and scaling laws, InstructGPT RLHF, to GPT-4 multimodal capabil
2026-03-01 · 36 min read #gpt#openai#language-model#transformer#pre-trainingLoRA: Efficient Fine-tuning of Large Language Models — Paper Analysis
A mathematical analysis of the core principles behind the LoRA (Low-Rank Adaptation) paper, along with practical implementation using HuggingFace PEFT.
2026-03-01 · 17 min read #ai-papers#lora#fine-tuning#llm#peftGAN Paper Deep Dive: How Generative Adversarial Networks Ushered in the Era of AI-Generated Content
From Ian Goodfellow's original GAN paper through DCGAN, WGAN, Progressive GAN, and StyleGAN -- we analyze the core theory of generative adversarial networks including the minimax game, Nash equilibrium, and training inst
2026-03-01 · 34 min read #gan#generative-model#adversarial-training#deep-learning#image-generationAttention Is All You Need - A Complete Analysis of the Transformer Paper
A thorough analysis of the "Attention Is All You Need" paper, the foundation of the Transformer architecture, breaking down its core mechanisms including Self-Attention, Multi-Head Attention, and Positional Encoding one
2026-03-01 · 18 min read #ai-papers#transformer#deep-learning#attentionFlashAttention: Optimizing Attention Through GPU Memory Hierarchy
A review of the FlashAttention paper with an in-depth analysis of IO-aware attention optimization leveraging the GPU HBM/SRAM memory hierarchy.
2026-03-01 · 18 min read #ai-papers#flash-attention#gpu#optimization#transformerOpenAI Codex Complete Analysis: From the Birth of AI Code Generation to the Evolution of Cloud Coding Agents
A complete analysis of OpenAI Codex covering the history of AI code generation and the evolution toward cloud-based coding agents.
2026-03-01 · 32 min read #codex#openai#code-generation#github-copilot#ai-codingMastering the Segment Anything Model: Paper Analysis and Practical Guide from SAM 1 to SAM 2 to SAM 3
A comprehensive deep dive into Meta AI's Segment Anything Model (SAM) series. Covering SAM 1 (image promptable segmentation), SAM 2 (real-time video segmentation), and SAM 3 (concept-aware segmentation) — including archi
2026-03-01 · 21 min read #sam#segment-anything#computer-vision#image-segmentation#video-segmentationResNet Paper In-Depth Analysis: How Residual Connections Broke the Depth Barrier in Deep Learning
An in-depth analysis of Microsoft Research ResNet paper. We cover the discovery of the degradation problem, the mathematical principles behind skip connections, bottleneck architecture, and the ImageNet ILSVRC 2015 winni
2026-03-01 · 29 min read #resnet#residual-learning#cnn#computer-vision#image-classificationRAG: Retrieval-Augmented Generation — Paper Analysis and Production Architecture
Analyzing the core concepts of the RAG paper and covering chunking strategies, Vector DB selection, and Advanced RAG patterns for designing production-level RAG systems.
2026-03-01 · 21 min read #ai-papers#rag#llm#vector-database#langchainComplete Analysis of the DDPM Paper: The Mathematics and Principles of Diffusion Models that Create Images from Noise
An in-depth analysis of the DDPM paper by Ho et al. We derive the Forward/Reverse diffusion process, variational lower bound (ELBO), noise scheduling, and simplified training objective from formulas, and provide a compre
2026-03-01 · 31 min read #ddpm#diffusion-model#generative-model#score-matching#stable-diffusionComplete Analysis of the BERT Paper: How Bidirectional Transformers Changed the Landscape of NLP
An in-depth analysis of Google BERT paper. We cover bidirectional pre-training through Masked Language Model (MLM) and Next Sentence Prediction (NSP), fine-tuning strategies, and the core principles of the architecture t
2026-03-01 · 28 min read #bert#nlp#transformer#pre-training#fine-tuningVision Transformer (ViT) Paper In-Depth Analysis: An Image is Worth 16x16 Words
An in-depth analysis of Google ViT paper. We cover the innovative approach of converting images into patch sequences, the principles behind Patch Embedding and Position Embedding, the performance vs. data efficiency trad
2026-03-01 · 30 min read #vit#vision-transformer#computer-vision#transformer#image-classification