Practical Weak Supervision: Doing More with Less Data

Most data scientists and engineers today rely on quality labeled data to train machine learning models. But building a training set manually is time-consuming and expensive, leaving many companies with unfinished ML projects. There's a more practical approach. In this book, Wee Hyong Tok, Amit Bahree, and Senja Filipi show you how to create products using weakly supervised learning models.

You'll learn how to build natural language processing and computer vision projects using weakly labeled datasets from Snorkel, a spin-off from the Stanford AI Lab. Because so many companies have pursued ML projects that never go beyond their labs, this book also provides a guide on how to ship the deep learning models you build.

• Get up to speed on the field of weak supervision, including ways to use it as part of the data science process
• Use Snorkel AI for weak supervision and data programming
• Get code examples for using Snorkel to label text and image datasets
• Use a weakly labeled dataset for text and image classification
• Learn practical considerations for using Snorkel with large datasets and using Spark clusters to scale labeling

大多數的資料科學家和工程師今天都依賴於高品質的標記資料來訓練機器學習模型。然而，手動建立訓練集是耗時且昂貴的，這使得許多公司無法完成他們的機器學習專案。這裡有一種更實用的方法。在這本書中，Wee Hyong Tok、Amit Bahree和Senja Filipi將向您展示如何使用弱監督學習模型來創建產品。

您將學習如何使用Snorkel這個源自斯坦福人工智慧實驗室的產品，使用弱標記的資料集來建立自然語言處理和電腦視覺專案。由於許多公司都在進行機器學習專案，但這些專案往往無法超越實驗室的範疇，本書還提供了一個指南，教您如何將您建立的深度學習模型部署到實際應用中。

本書的內容包括：
- 瞭解弱監督學習領域，包括如何將其作為資料科學流程的一部分
- 使用Snorkel AI進行弱監督學習和資料編程
- 提供使用Snorkel標記文本和圖像資料集的程式碼範例
- 使用弱標記的資料集進行文本和圖像分類
- 學習在處理大型資料集時使用Snorkel的實際考量，以及使用Spark集群來擴展標記的方法。

is a product and AI leader with a background in product management, machine learning/deep learning, research, and working on complex technical engagements with customers. Over the years, he has demonstrated that the early thought-leadership whitepapers he wrote on tech trends have become reality, and are deeply integrated into many products. Wee Hyong has worn many hats in his career--developer, program/product manager, data scientist, researcher, and strategist, and his range of experience has given him unique superpowers to lead and define the strategy for high-performing data and AI innovation teams.

Wee Hyong是一位產品和人工智慧領域的領導者，具有產品管理、機器學習/深度學習、研究以及與客戶進行複雜技術合作的背景。多年來，他所撰寫的早期技術趨勢白皮書已經成為現實，並深度融入許多產品中。Wee Hyong在職業生涯中擔任過多種角色，包括開發人員、程式/產品經理、資料科學家、研究員和策略師，他的豐富經驗使他具備領導和定義高效數據和人工智慧創新團隊戰略的獨特能力。