Training & Fine-Tuning explicadores.
Esqueça as docs de 40 páginas. Cada explicador transforma uma ideia complicada de IA, Claude Code, MCP ou cloud num diagrama animado ao vivo que você arrasta, scruba e quebra — até o conceito clicar em minutos, não em horas.
Todos os explicadores de Training & Fine-Tuning
Gradient Descent: Rolling Downhill to a Smarter Model
Training is a marble rolling down a wrinkled hill — the loss landscape. Tune learning rate and momentum to see it slide, oscillate, or get stuck.
Fine-Tuning vs RAG: When to Teach, When to Look Up
Fine-tuning changes what the model knows; RAG gives it a reference shelf at query time. Most "make the LLM know our docs" jobs are RAG jobs.
LoRA: Cheap Fine-Tuning Without Touching the Whole Model
LoRA freezes the giant model and trains tiny rank-r adapters next to it. 7B-param model, ~1% of the trainable weights, 99% of the quality.
Knowledge Distillation: Teaching a Small Model to Imitate a Big One
Distillation trains a small student model to mimic a big teacher's soft outputs. You ship the small one — much cheaper, surprisingly close in quality.
Pare de ler sobre isso. Comece a scrubar.
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