Computer Vision Projects with Pytorch: Design and Develop Production-Grade Models (Paperback)

Design and develop end-to-end, production-grade computer vision projects for real-world industry problems. This book discusses computer vision algorithms and their applications using PyTorch.
The book begins with the fundamentals of computer vision: convolutional neural nets, RESNET, YOLO, data augmentation, and other regularization techniques used in the industry. And then it gives you a quick overview of the PyTorch libraries used in the book. After that, it takes you through the implementation of image classification problems, object detection techniques, and transfer learning while training and running inference. The book covers image segmentation and an anomaly detection model. And it discusses the fundamentals of video processing for computer vision tasks putting images into videos. The book concludes with an explanation of the complete model building process for deep learning frameworks using optimized techniques with highlights on model AI explainability.
After reading this book, you will be able to build your own computer vision projects using transfer learning and PyTorch.

What You Will Learn

- Solve problems in computer vision with PyTorch.
- Implement transfer learning and perform image classification, object detection, image segmentation, and other computer vision applications
- Design and develop production-grade computer vision projects for real-world industry problems
- Interpret computer vision models and solve business problems

Who This Book Is For
Data scientists and machine learning engineers interested in building computer vision projects and solving business problems

設計和開發端到端的、生產級的計算機視覺專案，解決現實世界的行業問題。本書討論了使用PyTorch的計算機視覺算法及其應用。

本書首先介紹了計算機視覺的基礎知識：卷積神經網絡、RESNET、YOLO、數據增強和其他行業中使用的正則化技術。然後，它簡要介紹了本書中使用的PyTorch庫。接著，它帶領讀者實現圖像分類問題、目標檢測技術和遷移學習，並進行訓練和推理。本書還涵蓋了圖像分割和異常檢測模型，並討論了將圖像轉換為視頻的計算機視覺任務的基礎知識。最後，本書解釋了使用優化技術構建深度學習框架的完整模型構建過程，並強調了模型的AI可解釋性。

閱讀本書後，您將能夠使用遷移學習和PyTorch構建自己的計算機視覺專案。

您將學到什麼：
- 使用PyTorch解決計算機視覺問題。
- 實現遷移學習，執行圖像分類、目標檢測、圖像分割和其他計算機視覺應用。
- 設計和開發生產級的計算機視覺專案，解決現實世界的行業問題。
- 解讀計算機視覺模型，解決業務問題。

本書適合對構建計算機視覺專案和解決業務問題感興趣的數據科學家和機器學習工程師。

Akshay R Kulkarni is an AI and machine learning (ML) evangelist and a thought leader. He has consulted for Fortune 500 and global enterprises to drive AI and data science-led strategic transformations. He is currently the manager of data science & AI at Publicis Sapien. He is a Google developer and author of the book Natural Language Processing Recipes (Apress). He is a regular speaker at major AI and data science conferences (including Strata, O’Reilly AI Conf, and GIDS). Akshay is a visiting faculty member for some of the top graduate institutes in India. In 2019, he was featured as one of the top 40 under 40 Data Scientists in India. In his spare time, he enjoys reading, writing, coding, and helping aspiring data scientists. He lives in Bangalore with his family.

Adarsha Shivananda is a senior data scientist on Indegene's product and technology team where he works on building machine learning and artificial intelligence (AI) capabilities for pharma products. He aims to build a pool of exceptional data scientists within and outside of the organization to solve problems through training programs, and always wants to stay ahead of the curve. Previously, he worked with Tredence Analytics and IQVIA. He has worked extensively in the pharma, healthcare, retail, and marketing domains. He lives in Bangalore and loves to read and teach data science.

Nitin Ranjan Sharma is a manager at Novartis, involved in leading a team to develop products using multi-modal techniques. He has been a consultant developing solutions for Fortune 500 companies, involved in solving complex business problems using machine learning and deep learning frameworks. His major focus area and core expertise are computer vision and solving some of the challenging business problems dealing with images and video data. Before Novartis, he was part of the data science team at Publicis Sapient, EY, and TekSystems Global Services. He is a regular speaker at data science communities and meet-ups and also an open-source contributor. He has also been training and mentoring data science enthusiasts.

Akshay R Kulkarni 是一位人工智慧和機器學習（ML）的倡導者和思想領袖。他曾為財富500強和全球企業提供諮詢服務，推動以AI和數據科學為基礎的戰略轉型。他目前擔任Publicis Sapien的數據科學和AI經理。他是一位Google開發者，也是書籍《自然語言處理食譜》（Apress）的作者。他經常在主要的AI和數據科學會議上演講（包括Strata、O'Reilly AI Conf和GIDS）。Akshay是印度一些頂尖研究所的客座教師。2019年，他被評為印度40位40歲以下的頂尖數據科學家之一。在閒暇時間，他喜歡閱讀、寫作、編程和幫助有志成為數據科學家的人。他與家人一起居住在班加羅爾。

Adarsha Shivananda 是Indegene產品和技術團隊的高級數據科學家，他致力於為製藥產品建立機器學習和人工智慧（AI）能力。他的目標是通過培訓計劃在組織內外建立一支優秀的數據科學家團隊，並始終保持領先。之前，他曾在Tredence Analytics和IQVIA工作。他在製藥、醫療保健、零售和營銷領域有豐富的工作經驗。他居住在班加羅爾，喜歡閱讀和教授數據科學。

Nitin Ranjan Sharma 是諾華制藥公司的經理，負責領導一個團隊開發使用多模態技術的產品。他曾擔任顧問，為財富500強公司開發解決方案，利用機器學習和深度學習框架解決複雜的業務問題。他的主要專注領域和核心專業是計算機視覺，解決與圖像和視頻數據相關的一些具有挑戰性的業務問題。在加入諾華之前，他曾在Publicis Sapient、EY和TekSystems Global Services的數據科學團隊工作。他經常在數據科學社區和聚會上演講，也是一位開源貢獻者。他還一直在培訓和指導數據科學愛好者。