Genetic algorithms and machine learning for programmers : create AI models and evolve solutions / Frances Buontempo.
نوع المادة : نصاللغة: الإنجليزية السلاسل:Pragmatic programmersالناشر:Raleigh, North Carolina : The Pragmatic Bookshelf, 2019وصف:viii, 218 pages : illustrations ; 24 cmنوع المحتوى:- text
- unmediated
- volume
- 9781680506204
- 168050620X
- QA76.623 .B866 2019
نوع المادة | المكتبة الحالية | رقم الطلب | رقم النسخة | حالة | تاريخ الإستحقاق | الباركود | |
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كتاب | UAE Federation Library | مكتبة اتحاد الإمارات General Collection | المجموعات العامة | QA76.623 .B866 2019 (إستعراض الرف(يفتح أدناه)) | C.1 | Library Use Only | داخل المكتبة فقط | 30030000005442 | ||
كتاب | UAE Federation Library | مكتبة اتحاد الإمارات General Collection | المجموعات العامة | QA76.623 .B866 2019 (إستعراض الرف(يفتح أدناه)) | C.2 | المتاح | 30030000005443 |
Browsing UAE Federation Library | مكتبة اتحاد الإمارات shelves, Shelving location: General Collection | المجموعات العامة إغلاق مستعرض الرف(يخفي مستعرض الرف)
QA76.612 .A68 2003 Principles of constraint programming / | QA76.618 .P75 2012 Principal concepts in applied evolutionary computation : emerging trends / | QA76.618 .P75 2012 Principal concepts in applied evolutionary computation : emerging trends / | QA76.623 .B866 2019 Genetic algorithms and machine learning for programmers : create AI models and evolve solutions / | QA76.623 .B866 2019 Genetic algorithms and machine learning for programmers : create AI models and evolve solutions / | QA76.625 B46 1998 Corba on the Web / | QA76.625 D47 2012 Internet & World Wide Web : how to program. |
Includes bibliographical and references and index.
Escape! Code your way out of a paper bag -- Decide! Find the paper bag -- Boom! Create a genetic algorithm -- Swarm! Build a nature-inspired swarm -- Colonize! Discover pathways -- Diffuse! Employ a stochastic model -- Buzz! Converge on one solution -- Alive! Create artificial life -- Dream! Explore CA with GA -- Optimize! Find the best.
Self-driving cars, natural language recognition, and online recommendation engines are all possible thanks to machine learning. Discover machine learning algorithms using a handful of self-contained recipes. Create your own genetic algorithms, nature-inspired swarms, Monte Carlo simulations, and cellular automata. Find minima and maxima, using hill climbing and simulated annealing. Try selection mathods, including tournament and roulette wheels. Learn about heuristics, fitness functions, metrics, and clusters.