Kini Parameter? Itumọ | AI Understanding

Itumọ

Iwọn ti o kọ ẹkọ ninu awoṣe ti o ni ipa awọn abajade rẹ.

Awọn ofin ti o jọmọ

Iye nọmba ti o kọ ẹkọ ti o ṣe iwọn awọn ifihan agbara ti n kọja nipasẹ nẹtiwọọki nkankikan.

Iṣatunṣe-Ṣiṣe-Tuning Fine daradara (PEFT)

Awọn ọna ti o ṣe adaṣe awọn awoṣe nipasẹ ikẹkọ ipin kekere ti awọn aye ti a ṣafikun.

Algorithmic Irẹjẹ

Aiṣododo eleto ni awọn abajade awoṣe ti o ṣẹlẹ nipasẹ data skewed, awọn arosinu, tabi awọn yiyan awoṣe.

Ifitonileti data

Ilana yiyan awọn afi tabi awọn abajade ibi-afẹde si data aise fun ikẹkọ abojuto.

Fekito ti n fihan iye paramita kọọkan yẹ ki o yipada lati dinku pipadanu.

Awọn aami itọkasi igbẹkẹle ti a lo lati ṣe ikẹkọ tabi ṣe iṣiro awọn abajade awoṣe.

Kọ ẹkọ diẹ sii ninu awọn itọsọna ọfẹ wa

Hard Parameter Sharing in Multi-Task Networks Gumbel-Softmax and Reparameterization LoRA and Parameter-Efficient Tuning Hyperparameter Tuning

Wo tun

Overfitting Open-Source Model Perplexity OCR (Optical Character Recognition)Pipeline Normalization Precision Neural Network

Ṣawakiri ni kikun AI Gilosari Ṣawari gbogbo awọn itọsọna AI ọfẹ