설명
This work explores the exciting intersection of machine learning and deep learning, leveraging powerful tools like Scikit-Learn and TensorFlow. The author guides readers through the essential concepts and practical techniques required to develop intelligent systems that can learn from data. By providing a hands-on approach, the content engages readers in building and fine-tuning their own models, making complex theories accessible and applicable.
As readers advance, they encounter a wealth of practical examples and case studies that illustrate the transformative potential of machine learning. This book serves as a comprehensive resource for both beginners and experienced practitioners looking to deepen their understanding and enhance their skills in this rapidly evolving field. Whether delving into supervised methods, unsupervised techniques, or the intricacies of neural networks, readers are equipped with the knowledge needed to tackle real-world challenges.
As readers advance, they encounter a wealth of practical examples and case studies that illustrate the transformative potential of machine learning. This book serves as a comprehensive resource for both beginners and experienced practitioners looking to deepen their understanding and enhance their skills in this rapidly evolving field. Whether delving into supervised methods, unsupervised techniques, or the intricacies of neural networks, readers are equipped with the knowledge needed to tackle real-world challenges.
책 세부 정보
형식
킨들
페이지
921 페이지
언어
영어
출판됨
3월 13, 2017
출판사
O'Reilly Media
판본
판본 3개
ISBN-10
1491962259
ISBN-13
9781491962251