{"product_id":"machine-learning-for-engineers-using-data-to-solve-problems-for-physical-systems","title":"Machine Learning for Engineers: Using data to solve problems for physical systems","description":"\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cblockquote\u003e\n\u003cbr\u003eEngineers and applied scientists must harness machine learning to solve complex problems. This text teaches state-of-the-art technologies from an engineering perspective, with examples and case studies in Python and scikit-learn. It benefits undergraduate engineering students and practicing engineers. \u003c\/blockquote\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\\n                                                            \u003cstrong\u003eFormat\u003c\/strong\u003e: Hardback\u003cbr\u003e\\n                              \u003cstrong\u003eLength\u003c\/strong\u003e: 247 pages\u003cbr\u003e\\n                              \u003cstrong\u003ePublication date\u003c\/strong\u003e: 22 September 2021\u003cbr\u003e\\n                              \u003cstrong\u003ePublisher\u003c\/strong\u003e: Springer Nature Switzerland AG\u003cbr\u003e\\n                          \u003c\/p\u003e\u003cp\u003e\u003cbr\u003eEngineers and applied scientists must embrace the transformative power of machine learning to tackle the increasingly intricate and data-driven challenges that arise. This comprehensive text equips students and practicing engineers from traditionally \"analog\" disciplines, such as mechanical, aerospace, chemical, nuclear, and civil, with state-of-the-art machine learning technologies. Dr. McClarren delves into these technologies from an engineering perspective, showcasing their specific value to engineers through practical examples based on physical systems. The book progresses from basic learning models to deep neural networks, gradually enhancing readers' ability to apply modern machine learning techniques to their current work and preparing them for future, as yet unknown, problems. Instead of a black box approach, the author teaches a broad range of techniques while emphasizing the types of problems best addressed by each. Real-world examples and case studies in areas like controls, dynamics, heat transfer, and other engineering applications are implemented using Python and the popular libraries scikit-learn and tensorflow, demonstrating how readers can apply the most up-to-date methods to their own challenges. This book serves as a valuable resource for undergraduate engineering students seeking to acquire the skills required by future employers, as well as practicing engineers looking to expand and update their problem-solving toolkit.\u003c\/p\u003e\u003cp\u003e\\n                            \u003cstrong\u003eWeight\u003c\/strong\u003e: 548g\\n                            \u003cbr\u003e\u003cstrong\u003eDimension\u003c\/strong\u003e: 162 x 242 x 22 (mm)\\n                            \u003cbr\u003e\u003cstrong\u003eISBN-13\u003c\/strong\u003e: 9783030703875\\n                            \u003cbr\u003e \u003cstrong\u003eEdition number\u003c\/strong\u003e: 1st ed. 2021\\n                          \u003c\/p\u003e","brand":"Ryan G. McClarren","offers":[{"title":"Hardback","offer_id":44103109411066,"sku":"9783030703875","price":58.3,"currency_code":"GBP","in_stock":true}],"url":"https:\/\/shulphink.com\/products\/machine-learning-for-engineers-using-data-to-solve-problems-for-physical-systems","provider":"Shulph Ink","version":"1.0","type":"link"}