{"product_id":"hyperparameter-optimization-in-machine-learning-make-your-machine-learning-and-deep-learning-models-more-efficient-paperback","title":"Hyperparameter Optimization in Machine Learning: Make Your Machine Learning and Deep Learning Models More Efficient - Paperback","description":"\u003cp\u003eby \u003cb\u003eTanay Agrawal\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eDive into hyperparameter tuning of machine learning models and focus on what hyperparameters are and how they work. This book discusses different techniques of hyperparameters tuning, from the basics to advanced methods.\u003c\/p\u003e \u003cp\u003eThis is a step-by-step guide to hyperparameter optimization, starting with what hyperparameters are and how they affect different aspects of machine learning models. It then goes through some basic (brute force) algorithms of hyperparameter optimization. Further, the author addresses the problem of time and memory constraints, using distributed optimization methods. Next you'll discuss Bayesian optimization for hyperparameter search, which learns from its previous history. \u003c\/p\u003e \u003cp\u003eThe book discusses different frameworks, such as Hyperopt and Optuna, which implements sequential model-based global optimization (SMBO) algorithms. During these discussions, you'll focus on different aspects such as creation of search spaces and distributed optimization of these libraries. \u003c\/p\u003e \u003cp\u003e\u003ci\u003eHyperparameter Optimization in Machine Learning\u003c\/i\u003e creates an understanding of how these algorithms work and how you can use them in real-life data science problems. The final chapter summaries the role of hyperparameter optimization in automated machine learning and ends with a tutorial to create your own AutoML script.\u003c\/p\u003e Hyperparameter optimization is tedious task, so sit back and let these algorithms do your work. \u003cp\u003e\u003cb\u003eWhat You Will Learn\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eDiscover how changes in hyperparameters affect the model's performance.\u003c\/li\u003e\n\u003cli\u003eApply different hyperparameter tuning algorithms to data science problems\u003c\/li\u003e\n\u003cli\u003eWork with Bayesian optimization methods to create efficient machine learning and deep learning models\u003c\/li\u003e\n\u003cli\u003eDistribute hyperparameter optimization using a cluster of machines\u003c\/li\u003e\n\u003cli\u003eApproach automated machine learning using hyperparameter optimization\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003e\u003cb\u003eWho This Book Is For \u003c\/b\u003e\u003c\/p\u003e Professionals and students working with machine learning. \u003cp\u003e\u003c\/p\u003e \u003cp\u003e\u003c\/p\u003e\u003ch3\u003eBack Jacket\u003c\/h3\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eDive into hyperparameter tuning of machine learning models and focus on what hyperparameters are and how they work. This book discusses different techniques of hyperparameters tuning, from the basics to advanced methods.\u003c\/p\u003e\u003cp\u003eThis is a step-by-step guide to hyperparameter optimization, starting with what hyperparameters are and how they affect different aspects of machine learning models. It then goes through some basic (brute force) algorithms of hyperparameter optimization. Further, the author addresses the problem of time and memory constraints, using distributed optimization methods. Next you'll discuss Bayesian optimization for hyperparameter search, which learns from its previous history.\u003c\/p\u003e\u003cp\u003eThe book discusses different frameworks, such as Hyperopt and Optuna, which implements sequential model-based global optimization (SMBO) algorithms. During these discussions, you'll focus on different aspects such as creation of search spaces and distributed optimization of these libraries.\u003c\/p\u003e\u003cp\u003e\u003ci\u003eHyperparameter Optimization in Machine Learning\u003c\/i\u003e creates an understanding of how these algorithms work and how you can use them in real-life data science problems. The final chapter summaries the role of hyperparameter optimization in automated machine learning and ends with a tutorial to create your own AutoML script.\u003c\/p\u003e\u003cp\u003eHyperparameter optimization is tedious task, so sit back and let these algorithms do your work. \u003c\/p\u003e\u003cp\u003eYou will: \u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eDiscover how changes in hyperparameters affect the model's performance.\u003c\/li\u003e\n\u003cli\u003eApply different hyperparameter tuning algorithms to data science problems\u003c\/li\u003e\n\u003cli\u003eWork with Bayesian optimization methods to create efficient machine learning and deep learning models\u003c\/li\u003e\n\u003cli\u003eDistribute hyperparameter optimization using a cluster of machines\u003c\/li\u003e\n\u003cli\u003eApproach automated machine learning using hyperparameter optimization\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003e\u003c\/p\u003e\u003ch3\u003eAuthor Biography\u003c\/h3\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eTanay is a deep learning engineer and researcher, who graduated in 2019 in Bachelor of Technology from SMVDU, J\u0026amp;K. He is currently working at Curl Hg on SARA, an OCR platform. He is also advisor to Witooth Dental Services and Technologies. He started his career at MateLabs working on an AutoML Platform, Mateverse. He has worked extensively on hyperparameter optimization. He has also delivered talks on hyperparameter optimization at conferences including PyData, Delhi and PyCon, India. \u003c\/p\u003e\u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 166\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 0.4 x 9.21 x 6.14 IN\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003eIllustrated:\u003c\/strong\u003e Yes\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e November 29, 2020\u003c\/div\u003e","brand":"Books by splitShops","offers":[{"title":"Default Title","offer_id":42709160296511,"sku":"9781484265789","price":71.26,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0105\/8226\/1823\/files\/20534645bcad61eb0a52ee6bfe04a5aa.webp?v=1765050399","url":"https:\/\/dhlswag.com\/products\/hyperparameter-optimization-in-machine-learning-make-your-machine-learning-and-deep-learning-models-more-efficient-paperback","provider":"BBB","version":"1.0","type":"link"}