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https://github.com/dpcc2017/NLP-Interview-Notes#53-batch_size设置-面试篇
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早停法 EarlyStopping 面试篇https://articles.zsxq.com/id_u31j73pqq773.html
https://github.com/dpcc2017/NLP-Interview-Notes#54-早停法-earlystopping-面试篇
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标签平滑法 LabelSmoothing 面试篇https://articles.zsxq.com/id_87tkbsbcwk1d.html
https://github.com/dpcc2017/NLP-Interview-Notes#55-标签平滑法-labelsmoothing-面试篇
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https://github.com/dpcc2017/NLP-Interview-Notes#56-bert-trick-面试篇
Bert 未登录词处理 面试篇https://articles.zsxq.com/id_3gbrn1bn19am.html
https://github.com/dpcc2017/NLP-Interview-Notes#561-bert-未登录词处理-面试篇
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BERT在输入层引入额外特征 面试篇https://articles.zsxq.com/id_gd208jzrpafg.html
https://github.com/dpcc2017/NLP-Interview-Notes#562-bert在输入层引入额外特征-面试篇
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关于BERT 继续预训练 面试篇https://articles.zsxq.com/id_03lsi10e8iim.html
https://github.com/dpcc2017/NLP-Interview-Notes#563-关于bert-继续预训练-面试篇
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BERT如何处理篇章级长文本 面试篇https://articles.zsxq.com/id_e5aaclwgbwue.html
https://github.com/dpcc2017/NLP-Interview-Notes#564-bert如何处理篇章级长文本-面试篇
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https://github.com/dpcc2017/NLP-Interview-Notes#六-prompt-tuning-面试篇
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https://github.com/dpcc2017/NLP-Interview-Notes#61-prompt-面试篇
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Prompt 文本生成 面试篇https://articles.zsxq.com/id_po1gopdolinx.html
https://github.com/dpcc2017/NLP-Interview-Notes#62-prompt-文本生成-面试篇
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LoRA 面试篇https://articles.zsxq.com/id_da8pumsjwbqw.html
https://github.com/dpcc2017/NLP-Interview-Notes#63-lora-面试篇
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PEFT(State-of-the-art Parameter-Efficient Fine-Tuning)面试篇https://articles.zsxq.com/id_2r4w85eov81e.html
https://github.com/dpcc2017/NLP-Interview-Notes#64-peftstate-of-the-art-parameter-efficient-fine-tuning面试篇
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https://github.com/dpcc2017/NLP-Interview-Notes#七llms-面试篇
【现在达模型LLM,微调方式有哪些?各有什么优缺点?https://articles.zsxq.com/id_i6uv0mtg4mah.html
https://github.com/dpcc2017/NLP-Interview-Notes#71-现在达模型llm微调方式有哪些各有什么优缺点
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GLM:ChatGLM的基座模型 常见面试题https://articles.zsxq.com/id_bwx8btw6h2p1.html
https://github.com/dpcc2017/NLP-Interview-Notes#72--glmchatglm的基座模型-常见面试题
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https://github.com/dpcc2017/NLP-Interview-Notes#一基础算法-常见面试篇
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BatchNorm vs LayerNorm 常见面试篇https://articles.zsxq.com/id_wbep87ht600b.html
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激活函数 常见面试篇https://github.com/dpcc2017/NLP-Interview-Notes/blob/main/BasicAlgorithm/%E6%BF%80%E6%B4%BB%E5%87%BD%E6%95%B0.md
正则化常见面试篇https://articles.zsxq.com/id_g6mir08c0s8d.html
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优化算法及函数 常见面试篇https://articles.zsxq.com/id_hqd9p17b6afk.html
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归一化 常见面试篇https://articles.zsxq.com/id_8iemf392t53n.html
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判别式(discriminative)模型 vs. 生成式(generative)模型 常见面试篇https://articles.zsxq.com/id_siv7mtg3573r.html
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https://github.com/dpcc2017/NLP-Interview-Notes#二机器学习算法篇-常见面试篇
逻辑回归 常见面试篇https://articles.zsxq.com/id_98g8ef7zir1q.html
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支持向量机 常见面试篇https://articles.zsxq.com/id_nqeiewjxovjq.html
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集成学习 常见面试篇https://articles.zsxq.com/id_iqq9rzq9ctcd.html
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【关于 Python 】那些你不知道的事https://github.com/dpcc2017/NLP-Interview-Notes/blob/main/python
https://github.com/dpcc2017/NLP-Interview-Notes#九关于-python-那些你不知道的事
【关于 Python 】那些你不知道的事https://github.com/dpcc2017/NLP-Interview-Notes/blob/main/python
【关于 Tensorflow 】那些你不知道的事https://github.com/dpcc2017/NLP-Interview-Notes/blob/main/Tensorflow
https://github.com/dpcc2017/NLP-Interview-Notes#十关于-tensorflow-那些你不知道的事
【关于 Tensorflow 损失函数】 那些你不知道的事https://github.com/dpcc2017/NLP-Interview-Notes/blob/main/Tensorflow/loss_study
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