Tag: #deep-learning
Writing on GPUs, LLMs, MLOps, Kubernetes — and mindset · 77 posts
CNN Architecture Complete Guide: From LeNet to EfficientNet and Vision Transformers
The complete guide to understanding the history and evolution of CNN architectures. Master every major model — LeNet, AlexNet, VGG, ResNet, DenseNet, EfficientNet, and Vision Transformer — through structural analysis and
2026-03-17 · 21 min read #cnn#computer-vision#deep-learning#resnet#vggAdversarial Machine Learning Guide: Complete Guide to Attacks and Defenses
A complete guide to mastering adversarial machine learning. Learn FGSM, PGD, C&W attacks, data poisoning, model extraction, backdoor attacks, and defense techniques including Adversarial Training and Certified Defenses,
2026-03-17 · 21 min read #adversarial-ml#ai-security#deep-learning#robustness#aiPyTorch Complete Guide: Zero to Hero — From Tensors to Distributed Training
A comprehensive guide to mastering PyTorch from the basics to advanced techniques. Learn tensor operations, automatic differentiation, CNN/RNN/Transformer implementation, and distributed training with practical examples
2026-03-17 · 21 min read #pytorch#deep-learning#ai#python#neural-networkDeep Learning Model Quantization Complete Guide: Master INT8, INT4, GPTQ, AWQ, GGUF
A comprehensive guide to mastering deep learning model quantization. Understand the principles from FP32 to INT8 and INT4 quantization, and master GPTQ, AWQ, GGUF, bitsandbytes, AutoGPTQ, and llama.cpp through practical
2026-03-17 · 23 min read #quantization#model-compression#gptq#awq#ggufGenerative AI Complete Guide: Master GANs, VAEs, and Diffusion Models
A complete guide to mastering the core architectures of generative AI. Understand VAE, GAN, DDPM diffusion models, and Stable Diffusion from the ground up with mathematical derivations and complete PyTorch implementation
2026-03-17 · 22 min read #generative-ai#gan#vae#diffusion-model#stable-diffusionGraph Neural Networks Complete Guide: GCN, GAT, GraphSAGE to Molecular Design
A complete guide to Graph Neural Networks from fundamentals to cutting-edge research. Covers graph theory, GCN, GraphSAGE, GAT, Graph Transformer, molecular design, and social network analysis with PyTorch Geometric impl
2026-03-17 · 23 min read #gnn#graph-neural-network#gcn#gat#pytorch-geometricDeep Learning Debugging Complete Guide: From Diagnosing Training Failures to Performance Optimization
A complete guide to systematically diagnosing and resolving deep learning training failures. Covers Loss NaN, vanishing/exploding gradients, overfitting, slow convergence, and out-of-memory errors with real-world code ex
2026-03-17 · 19 min read #deep-learning#debugging#pytorch#training#optimizationDeep Learning Training Methods Complete Guide: From Optimization to Distributed Training
A comprehensive guide covering all techniques for effectively training deep learning models. Learn gradient descent, optimizers, learning rate scheduling, regularization, batch normalization, transfer learning, fine-tuni
2026-03-17 · 28 min read #deep-learning#training#optimization#regularization#distributed-trainingMultimodal AI Complete Guide: Master CLIP, LLaVA, GPT-4V, and Gemini Vision
A complete guide to mastering multimodal AI from fundamentals to the latest vision-language models. Learn CLIP, BLIP-2, LLaVA, InstructBLIP, GPT-4V, Gemini Vision, and Claude Vision with practical code, plus Multimodal R
2026-03-17 · 25 min read #multimodal#vision-language#clip#llava#gpt-4vMath + LaTeX/KaTeX Complete Guide for Reading AI/ML Papers
A comprehensive summary of essential math concepts (linear algebra, calculus, probability/statistics, optimization) and LaTeX/KaTeX formula syntax for reading AI/ML papers with practical examples, symbol cheat sheets, an
2026-03-08 · 20 min read #ai-papers#math#latex#katex#linear-algebraKAN (Kolmogorov-Arnold Networks) Paper Analysis: Learnable Activation Function Architecture Replacing MLPs
In-depth analysis of the KAN paper. Covers the mathematical background of the Kolmogorov-Arnold representation theorem, B-spline activation functions, accuracy and interpretability comparison with MLPs, and practical imp
2026-03-08 · 38 min read #ai-papers#kan#kolmogorov-arnold#neural-network#mlpBuild Your Own GPT — Training a Language Model from Scratch with nanoGPT
Train a GPT language model from scratch using Andrej Karpathy's nanoGPT. A complete dissection of the Transformer architecture — tokenizers, Self-Attention, training loops — all with code.
2026-03-03 · 7 min read #ai#llm#gpt#nanogpt#transformerComplete Math Guide for AI — From Linear Algebra to Information Theory
A guide to the math needed for AI/deep learning, explained with code and intuition. Linear algebra (matrices, eigenvalues), calculus (partial derivatives, backpropagation), probability/statistics (Bayes, distributions),
2026-03-02 · 13 min read #ai#mathematics#linear-algebra#calculus#probabilityMamba Paper Review: Going Beyond Transformers with Selective State Space Models
An in-depth review of the Mamba (Selective State Space Models) paper. Analyzing the evolution from S4 to Mamba, the Selective Scan mechanism, Hardware-Aware algorithms, and Mamba-2 State Space Duality with code examples.
2026-03-02 · 8 min read #mamba#state-space-model#ssm#transformer#linear-attentionMamba: 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#transformerComplete Guide to Text-to-Image Model Training Methodologies: From GAN to Flow Matching
An in-depth, paper-based analysis of training methodologies for Text-to-Image generative model architectures spanning GAN, VAE, Diffusion, and Flow Matching. Comprehensively covers training strategies and fine-tuning tec
2026-03-01 · 51 min read #deep-learning#text-to-image#diffusion#stable-diffusion#generative-aiComplete Guide to Building a Linux GPU Server for Deep Learning
A step-by-step guide to building a Linux GPU server for deep learning development, covering everything from NVIDIA driver installation to Docker GPU environments, based on official NVIDIA documentation.
2026-03-01 · 20 min read #linux#gpu#cuda#nvidia-driver#deep-learningNVIDIA GPU Operator Complete Guide: Components, Installation, and KubeVirt GPU Passthrough
A detailed analysis of NVIDIA GPU Operator architecture and the roles of its 7 core components (Driver, Container Toolkit, Device Plugin, DCGM, MIG Manager, Node Feature Discovery, GFD), covering Helm-based installation,
2026-03-01 · 18 min read #gpu-operator#nvidia#kubernetes#kubevirt#gpuWan Text-to-Video/Image-to-Video and Z Image Turbo Complete Analysis: Architecture and Applications of Next-Gen Video/Image Generation Models
A complete analysis of Wan video generation models and Z Image Turbo covering architecture, performance, and practical applications.
2026-03-01 · 36 min read #wan#text-to-video#image-to-video#z-image-turbo#video-generationComplete Guide to Multi-GPU Distributed Training: DDP, FSDP, DeepSpeed
Systematically analyze the core components of multi-GPU distributed training including DDP, FSDP, and DeepSpeed ZeRO based on PyTorch official documentation, with practical setup instructions.
2026-03-01 · 21 min read #gpu#cuda#distributed-training#deep-learning#pytorch