Tag: #training

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• Do You Need a New Learning Rate When You Change LoRA Rank? — The Two Regimes μA (2026) Splits, and the Limits of That Evidence ♪ Listenable

  Two competing beliefs circulate in practice at the same time: that changing the rank in LoRA means you have to re-find the optimal learning rate, and that using 1/r scaling makes the learning rate independent of rank. Th

  2026-07-16 · 18 min read #llm#lora#fine-tuning#peft#training

• The Complete Guide to LLM Training Data Preprocessing — From Web Crawls to Token Packing, with the Latest Papers

  Good models come from good data, and good data comes from a preprocessing pipeline. This post walks through the entire pretraining data process step by step — web crawl collection → text extraction → language identificat

  2026-07-09 · 7 min read #ai#llm#data-engineering#preprocessing#training

• AI Model Development, Start to Finish — a Realistic Lifecycle from Data to Deployment

  Model development starts not with pretraining but with a decision ladder — does prompting suffice, does RAG suffice, do you need fine-tuning? The eval-first principle of building your test set before your model, data qua

  2026-07-07 · 8 min read #ai#ml#llm#mlops#training

• Training Vision LLMs — How to Teach Input and Output

  A vision-language model is trained in stages, from alignment pretraining to instruction fine-tuning. We organize what gets taught and how, from the angle of the training pipeline: vision encoder freezing strategy, data c

  2026-06-26 · 17 min read #mlops#vision-language-model#multimodal#training#instruction-tuning

• Building an LLM from Scratch — A Stanford CS336 Style Learning Roadmap

  Stanford CS336 (Language Modeling from Scratch) keeps hitting the Hacker News front page, and from-scratch LLM building is hot again. This post dissects the full curriculum — tokenizers, attention, distributed training,

  2026-06-12 · 15 min read #llm#transformer#cs336#deep-learning#tokenizer

• Sports Science and Periodization Training: A Complete Guide to Elite Athletic Principles ♪ Listenable

  A comprehensive guide covering training principles used by elite athletes, periodization frameworks, strength and power science, recovery strategies, and sports nutrition — with practical programs applicable to everyday

  2026-03-17 · 21 min read #sports-science#training#periodization#exercise-physiology#recovery

• Deep Learning Debugging Complete Guide: From Diagnosing Training Failures to Performance Optimization ♪ Listenable

  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#optimization

• Deep Learning Training Methods Complete Guide: From Optimization to Distributed Training ♪ Listenable

  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-training

• Complete Guide to Text-to-Image Model Training Methodologies: From GAN to Flow Matching ♪ Listenable

  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-ai