AI for Healthcare with Keras and Tensorflow 2.0: Design, Develop, and Deploy Machine Learning Models Using Healthcare Data
暫譯: 使用 Keras 和 TensorFlow 2.0 的醫療保健 AI：設計、開發和部署基於醫療數據的機器學習模型
Anshik
- 出版商: Apress
- 出版日期: 2021-06-26
- 售價: $1,900
- 貴賓價: 9.5 折 $1,805
- 語言: 英文
- 頁數: 381
- 裝訂: Quality Paper - also called trade paper
- ISBN: 1484270851
- ISBN-13: 9781484270851
-
相關分類:
DeepLearning、TensorFlow、人工智慧、Machine Learning
立即出貨 (庫存=1)
買這商品的人也買了...
-
$825Inside the Microsoft Build Engine: Using MSBuild and Team Foundation Build (Paperback) -
圖解電池入門$280$238 -
Mastering Windows 8 C++ App Development$2,050$1,948 -
$352密碼學 (C\C++語言實現原書第2版) -
世界第一簡單電池$280$238 -
基於 ARM Cortex-M3 的 STM32 微控制器實戰教程, 2/e$210$200 -
Deploy Containers on Aws: With Ec2, Ecs, and Eks (Paperback)$2,990$2,841 -
AI 自動化測試：技術原理、平臺搭建與工程實踐$534$507 -
$505數據挖掘與機器學習 : PMML 建模 (下) -
$446數據挖掘與機器學習 : PMML 建模 (上) -
$505深入淺出 Embedding：原理解析與應用實踐 -
複利的喜悅：從價值投資到人生決策，啟發巴菲特、蒙格等投資典範的穩健致富金律$620$527 -
嵌入式技術應用項目式教程（STM32版）$234$222 -
$1,584Practical AI for Healthcare Professionals: Machine Learning with Numpy, Scikit-learn, and TensorFlow -
$998Python for Cybersecurity: Using Python for Cyber Offense and Defense -
Keras 大神歸位：深度學習全面進化！用 Python 實作 CNN、RNN、GRU、LSTM、GAN、VAE、Transformer$1,200$948 -
$449物聯網及低功耗藍牙5.x高級開發 -
Hands-On Visual Studio 2022: A developer's guide to exploring new features and best practices in VS2022 for maximum productivity (Paperback)$2,730$2,594 -
基於 ARM Cortex-M3 的 STM32 嵌入式系統原理及應用$414$393 -
$221STM32 單片機原理與應用 -
ChatGPT 與 AI繪圖效率大師：從日常到職場的全方位應用總整理，48小時迎接減壓新生活！$620$484 -
ChatGPT 領軍 DALL-E 2 + Midjourney + D-ID + Synthesia：邁向 AI文字、圖像、影片之路 (全彩印刷)$500$395 -
都問 AI 吧！ChatGPT 上手的第一本書$380$285 -
$301嵌入式技術及應用 (STM32CubeMX版) -
高速省電 CPU 的未來 - STM32F103 嵌入式 Arm 系統專案實作$1,000$790
商品描述
Learn how AI impacts the healthcare ecosystem through real-life case studies with TensorFlow 2.0 and other machine learning (ML) libraries.
This book begins by explaining the dynamics of the healthcare market, including the role of stakeholders such as healthcare professionals, patients, and payers. Then it moves into the case studies. The case studies start with EHR data and how you can account for sub-populations using a multi-task setup when you are working on any downstream task. You also will try to predict ICD-9 codes using the same data. You will study transformer models. And you will be exposed to the challenges of applying modern ML techniques to highly sensitive data in healthcare using federated learning. You will look at semi-supervised approaches that are used in a low training data setting, a case very often observed in specialized domains such as healthcare. You will be introduced to applications of advanced topics such as the graph convolutional network and how you can develop and optimize image analysis pipelines when using 2D and 3D medical images. The concluding section shows you how to build and design a closed-domain Q&A system with paraphrasing, re-ranking, and strong QnA setup. And, lastly, after discussing how web and server technologies have come to make scaling and deploying easy, an ML app is deployed for the world to see with Docker using Flask.
By the end of this book, you will have a clear understanding of how the healthcare system works and how to apply ML and deep learning tools and techniques to the healthcare industry. What You Will Learn
Data scientists and software developers interested in machine learning and its application in the healthcare industry
This book begins by explaining the dynamics of the healthcare market, including the role of stakeholders such as healthcare professionals, patients, and payers. Then it moves into the case studies. The case studies start with EHR data and how you can account for sub-populations using a multi-task setup when you are working on any downstream task. You also will try to predict ICD-9 codes using the same data. You will study transformer models. And you will be exposed to the challenges of applying modern ML techniques to highly sensitive data in healthcare using federated learning. You will look at semi-supervised approaches that are used in a low training data setting, a case very often observed in specialized domains such as healthcare. You will be introduced to applications of advanced topics such as the graph convolutional network and how you can develop and optimize image analysis pipelines when using 2D and 3D medical images. The concluding section shows you how to build and design a closed-domain Q&A system with paraphrasing, re-ranking, and strong QnA setup. And, lastly, after discussing how web and server technologies have come to make scaling and deploying easy, an ML app is deployed for the world to see with Docker using Flask.
By the end of this book, you will have a clear understanding of how the healthcare system works and how to apply ML and deep learning tools and techniques to the healthcare industry. What You Will Learn
- Get complete, clear, and comprehensive coverage of algorithms and techniques related to case studies
- Look at different problem areas within the healthcare industry and solve them in a code-first approach
- Explore and understand advanced topics such as multi-task learning, transformers, and graph convolutional networks
- Understand the industry and learn ML
Data scientists and software developers interested in machine learning and its application in the healthcare industry
商品描述(中文翻譯)
學習人工智慧如何透過真實案例研究影響醫療生態系統，使用 TensorFlow 2.0 及其他機器學習 (ML) 函式庫。本書首先解釋醫療市場的動態，包括醫療專業人員、病患和支付者等利害關係人的角色。接著進入案例研究。案例研究從電子健康紀錄 (EHR) 數據開始，並探討在處理任何下游任務時，如何使用多任務設置來考量子族群。您還將嘗試使用相同的數據預測 ICD-9 代碼。您將學習變壓器模型 (transformer models)。此外，您將接觸到將現代機器學習技術應用於醫療中高度敏感數據的挑戰，並使用聯邦學習 (federated learning)。您將研究在低訓練數據環境下使用的半監督方法，這在醫療等專業領域中非常常見。您將了解進階主題的應用，例如圖卷積網絡 (graph convolutional network)，以及如何在使用 2D 和 3D 醫療影像時開發和優化影像分析管道。最後一部分將展示如何建立和設計一個封閉領域的問答系統，包含改寫、重新排序和強大的問答設置。最後，在討論網頁和伺服器技術如何使擴展和部署變得簡單後，將使用 Flask 和 Docker 部署一個機器學習應用程式，讓全世界都能看到。
在本書結束時，您將清楚了解醫療系統的運作方式，以及如何將機器學習和深度學習工具及技術應用於醫療產業。
您將學到什麼
- 全面、清晰且詳盡地涵蓋與案例研究相關的演算法和技術
- 查看醫療產業中的不同問題領域，並以程式碼為先的方式解決它們
- 探索並理解進階主題，如多任務學習、變壓器和圖卷積網絡
- 了解產業並學習機器學習
本書適合誰
對機器學習及其在醫療產業中的應用感興趣的數據科學家和軟體開發人員
作者簡介
Anshik has a deep passion for building and shipping data science solutions that create great business value. He is currently working as a senior data scientist at ZS Associates and is a key member on the team developing core unstructured data science capabilities and products. He has worked across industries such as pharma, finance, and retail, with a focus on advanced analytics. Besides his day-to-day activities, which involve researching and developing AI solutions for client impact, he works with startups as a data science strategy consultant. Anshik holds a bachelor's degree from Birla Institute of Technology & Science, Pilani. He is a regular speaker at AI and machine learning conferences. He enjoys trekking and cycling.
作者簡介(中文翻譯)
Anshik 對於構建和交付能創造巨大商業價值的數據科學解決方案充滿熱情。他目前在 ZS Associates 擔任高級數據科學家，是開發核心非結構化數據科學能力和產品的團隊關鍵成員。他曾在製藥、金融和零售等行業工作，專注於高級分析。除了日常的研究和開發 AI 解決方案以影響客戶外，他還作為數據科學策略顧問與初創公司合作。Anshik 擁有比爾拉科技與科學學院（Birla Institute of Technology & Science, Pilani）的學士學位。他是 AI 和機器學習會議的常規演講者，並喜歡健行和騎自行車。
