Course Code: pretrainedmodels
Duration: 14 hours
Prerequisites:
  • 對機器學習概念的基本理解
  • 熟悉 Python 程式設計
  • 使用庫進行數據處理的基本知識,如 Pandas

觀眾

  • 數據科學家
  • 人工智慧愛好者
Overview:

預訓練模型是現代 AI 的基石,提供可適應各種應用的預構建功能。本課程向參與者介紹預訓練模型的基礎知識、架構和實際用例。參與者將學習如何利用這些模型完成文本分類、圖像識別等任務。

這種由講師指導的現場培訓(在線或現場)面向初級專業人員,他們希望瞭解預訓練模型的概念,並學習如何應用它們來解決實際問題,而無需從頭開始構建模型。

在本次培訓結束時,參與者將能夠:

  • 瞭解預訓練模型的概念和優勢。
  • 探索各種預先訓練的模型架構及其使用案例。
  • 針對特定任務微調預先訓練的模型。
  • 在簡單的機器學習項目中實施預訓練模型。

課程形式

  • 互動講座和討論。
  • 大量的練習和練習。
  • 在即時實驗室環境中動手實施。

課程自定義選項

  • 要申請本課程的定製培訓,請聯繫我們進行安排。
Course Outline:

Introduction to Pre-trained Models

  • 什麼是預訓練模型?
  • 使用預訓練模型的好處
  • 流行的預訓練模型(例如 BERT、ResNet)概述

瞭解預訓練模型架構

  • 模型架構基礎知識
  • 遷移學習和微調概念
  • 如何構建和訓練預訓練模型

設置環境

  • 安裝和配置 Python 和相關庫
  • 探索預先訓練的模型存儲庫(例如 Hugging Face)
  • 載入和測試預訓練模型

動手操作預訓練模型

  • 使用預先訓練的模型進行文本分類
  • 將預訓練模型應用於圖像識別任務
  • 為自訂數據集微調預訓練模型

部署預訓練模型

  • 匯出和保存微調模型
  • 將模型整合到應用程式中
  • 在生產環境中部署模型的基礎知識

挑戰和最佳實踐

  • 瞭解模型限制
  • 在微調過程中避免過擬合
  • 確保合乎道德地使用 AI 模型

預訓練模型的未來趨勢

  • 新興架構及其應用
  • 遷移學習的進步
  • 探索大型語言模型和多模態模型

總結和後續步驟

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