Machine Learning Algorithms in Depth
Smolyakov, Vadim
- 出版商: Manning
- 出版日期: 2024-08-27
- 售價: $2,920
- 貴賓價: 9.5 折 $2,774
- 語言: 英文
- 頁數: 328
- 裝訂: Quality Paper - also called trade paper
- ISBN: 1633439216
- ISBN-13: 9781633439214
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相關分類:
Machine Learning、Algorithms-data-structures
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相關主題
商品描述
- Monte Carlo Stock Price Simulation
- Image Denoising using Mean-Field Variational Inference
- EM algorithm for Hidden Markov Models
- Imbalanced Learning, Active Learning and Ensemble Learning
- Bayesian Optimization for Hyperparameter Tuning
- Dirichlet Process K-Means for Clustering Applications
- Stock Clusters based on Inverse Covariance Estimation
- Energy Minimization using Simulated Annealing
- Image Search based on ResNet Convolutional Neural Network
- Anomaly Detection in Time-Series using Variational Autoencoders
Machine Learning Algorithms in Depth dives into the design and underlying principles of some of the most exciting machine learning (ML) algorithms in the world today. With a particular emphasis on probability-based algorithms, you'll learn the fundamentals of Bayesian inference and deep learning. You'll also explore the core data structures and algorithmic paradigms for machine learning. Each algorithm is fully explored with both math and practical implementations so you can see how they work and how they're put into action. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Fully understanding how machine learning algorithms function is essential for any serious ML engineer. This vital knowledge lets you modify algorithms to your specific needs, understand the tradeoffs when picking an algorithm for a project, and better interpret and explain your results to your stakeholders. This unique guide will take you from relying on one-size-fits-all ML libraries to developing your own algorithms to solve your business needs. About the book Machine Learning Algorithms in Depth dives deep into the how and the why of machine learning algorithms. For each category of algorithm, you'll go from math-first principles to a hands-on implementation in Python. You'll explore dozens of examples from across all the fields of machine learning, including finance, computer vision, NLP, and more. Each example is accompanied by worked-out derivations and details, as well as insightful code samples and graphics. By the time you're done reading, you'll know how major algorithms work under the hood--and be a better machine learning practitioner for it. About the reader For intermediate machine learning practitioners familiar with linear algebra, probability, and basic calculus. About the author Vadim Smolyakov is a data scientist in the Enterprise & Security DI R&D team at Microsoft. He is a former PhD student in AI at MIT CSAIL with research interests in Bayesian inference and deep learning. Prior to joining Microsoft, Vadim developed machine learning solutions in the e-commerce space.
商品描述(中文翻譯)
發展對機器學習演算法運作的數學直覺,以便改善模型性能並有效排解複雜的機器學習問題。
在《深入探討機器學習演算法》中,您將探索數十種機器學習演算法的實際實現,包括:
- 蒙地卡羅股票價格模擬
- 使用均場變分推斷的圖像去噪
- 隱馬可夫模型的EM演算法
- 不平衡學習、主動學習和集成學習
- 超參數調整的貝葉斯優化
- 用於聚類應用的狄利克雷過程K均值
- 基於逆協方差估計的股票聚類
- 使用模擬退火的能量最小化
- 基於ResNet卷積神經網絡的圖像搜索
- 使用變分自編碼器的時間序列異常檢測
《深入探討機器學習演算法》深入探討當今世界上一些最令人興奮的機器學習(ML)演算法的設計和基本原則。特別強調基於概率的演算法,您將學習貝葉斯推斷和深度學習的基本原理。您還將探索機器學習的核心數據結構和演算法範式。每個演算法都將全面探討,包括數學和實際實現,讓您了解它們的運作方式及其應用。
購買印刷版書籍可獲得Manning Publications提供的免費PDF、Kindle和ePub格式電子書。
關於技術
充分理解機器學習演算法的運作對任何認真的機器學習工程師來說都是必不可少的。這一重要知識使您能夠根據特定需求修改演算法,理解在為項目選擇演算法時的權衡,並更好地解釋和說明您的結果給利益相關者。這本獨特的指南將帶您從依賴通用的機器學習庫,發展到開發自己的演算法以解決商業需求。
關於本書
《深入探討機器學習演算法》深入探討機器學習演算法的運作原理和原因。對於每一類演算法,您將從數學原則開始,進而在Python中進行實作。您將探索來自金融、計算機視覺、自然語言處理等各個機器學習領域的數十個範例。每個範例都附有詳細的推導和細節,以及有見地的程式碼範例和圖形。當您讀完這本書時,您將了解主要演算法的內部運作,並成為更優秀的機器學習實踐者。
關於讀者
適合熟悉線性代數、概率和基本微積分的中級機器學習實踐者。
關於作者
Vadim Smolyakov是微軟企業與安全DI R&D團隊的數據科學家。他曾是MIT CSAIL人工智慧的博士生,研究興趣包括貝葉斯推斷和深度學習。在加入微軟之前,Vadim在電子商務領域開發機器學習解決方案。
作者簡介
Vadim Smolyakov is a data scientist in Enterprise & Security DI R&D team at Microsoft. He is a former PhD student in AI at MIT CSAIL with research interests in Bayesian inference and deep learning. Prior to joining Microsoft, Vadim developed machine learning solutions in the e-commerce space.
作者簡介(中文翻譯)
Vadim Smolyakov 是微軟企業與安全 DI 研發團隊的數據科學家。他曾是麻省理工學院 CSAIL 的人工智慧博士生,研究興趣包括貝葉斯推斷和深度學習。在加入微軟之前,Vadim 在電子商務領域開發了機器學習解決方案。