Machine learning algorithms models (35 Ergebnisse)

Paperback. Zustand: Good. No Jacket. Pages can have notes/highlighting. Spine may show signs of wear. ~ ThriftBooks: Read More, Spend Less.

paperback. Zustand: Very Good.

Paperback. Zustand: Very Good. Machine Learning: Complete Beginners Guide For Neural Networks, Algorithms, Random Forests and Decision Trees Made Simple: Volume 1 (Algorithms,markov models,data analytics) This book is in very good condition and will be shipped within 24 hours of ordering. The cover may have some limited signs of wear but the pages are clean, intact and the spine remains undamaged. This book has clearly been well maintained and looked after thus far. Money back guarantee if you are not satisfied. See all our books here, order more than 1 book and get discounted shipping. .…

Paperback. Zustand: Very Good. The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.

Paperback. Zustand: Very Good. Shipped within 24 hours from our UK warehouse. Clean, undamaged book with no damage to pages and minimal wear to the cover. Spine still tight, in very good condition. Remember if you are not happy, you are covered by our 100% money back guarantee.

Zustand: New. In English.

Zustand: New. In English.

Zustand: New. *Price HAS BEEN REDUCED by 10% until Monday, Sept. 28 (weekend SALE item)* 378 pp., hardcover, new, THIS IS THE 2019 PRINTING. - If you are reading this, this item is actually (physically) in our stock and ready for shipment once ordered. We are not bookjackers. Buyer is responsible for any additional duties, taxes, or fees required by recipient's country.…

Zustand: New. In English.

Taschenbuch. Zustand: Neu. Neuware.

Taschenbuch. Zustand: Neu. Neuware - Master Machine Learning and Artificial Intelligence through 1,000 carefully crafted multiple-choice questions designed for students, professionals, educators, researchers, and competitive exam aspirants.This comprehensive practice book provides structured learning from beginner to advanced level, making it an ideal resource for self-study, university courses, certification preparation, technical interviews, and competitive examinations.The book covers Artificial Intelligence fundamentals, Machine Learning fundamentals, data preparation, feature engineering, supervised learning, unsupervised learning, reinforcement learning, model evaluation, decision trees, random forests, support vector machines, k-nearest neighbors, naïve Bayes, ensemble learning, deep learning, artificial neural networks, convolutional neural networks, recurrent neural networks, long short-term memory networks, transformers, natural language processing, computer vision, recommendation systems, explainable AI, generative AI, large language models, prompt engineering, robotics, Edge AI, MLOps, AI ethics, responsible AI, AI governance, Artificial General Intelligence, Quantum AI, real-world AI applications, and future trends in Artificial Intelligence.Every question includes four multiple-choice options, the correct answer, and a detailed explanation to strengthen conceptual understanding and practical knowledge.This book is suitable for university students, computer science students, engineering students, data science learners, software engineers, AI professionals, researchers, competitive examination candidates, certification candidates, technical interview preparation, and self-learners.Whether you are beginning your journey into Artificial Intelligence or preparing for examinations, certifications, or job interviews, this book provides a comprehensive and practical learning experience that builds confidence and develops problem-solving skills.…

Paperback. Zustand: Brand New. 218 pages. 9.50x7.75x0.75 inches. In Stock.

Zustand: New. In English.

Zustand: New. In English.

Zustand: New.

Zustand: New.

Kartoniert / Broschiert. Zustand: New. KlappentextRefuel your AI Models and ML applications with High-Quality Optimization and Search SolutionsKey FeaturesComplete coverage on practical implementation of genetic algorithms.Intuitive explanations and visualizations supply theo.

Zustand: New. In English.

Zustand: very good. Le livre peut montrer des signes d'usure dus a une utilisation constante, etre marque, porter des marques d'identification ou presenter plusieurs dommages esthetiques mineurs. vendeur professionnel; envoi soigne en 24/48h.

paperback. Zustand: Sehr gut. 798 Seiten; 9781838820299.2 Gewicht in Gramm: 3.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Doctoral Thesis / Dissertation from the year 2017 in the subject Engineering - Computer Engineering, grade: 100.00/100.00, Çukurova University, language: English, abstract: The purpose of this thesis is twofold. The first purpose is to develop new hybrid feature selection-based maximal oxygen uptake (VO2max) prediction models using for the first time the double and triple combinations of maximal, submaximal and questionnaire variables. Several machine learning methods including Support Vector Machine, artificial neural network-based and tree-structured methods combined individually with three feature selectors Relief-F, minimum redundancy maximum relevance (mRMR) and maximum-likelihood feature selector (MLFS) have been applied for model development. The second purpose is to design a new ensemble feature selector, which aggregates the consensus properties of Relief-F, mRMR and MLFS to produce more robust decisions about the set of relevantly identified VO2max predictors and to create more accurate prediction models. Using 10-fold cross validation on three different datasets, the performance of prediction models has been evaluated by calculating their multiple correlation coefficients (R's) and root mean squared errors (RMSE's). The results show that compared with the results of the other regular feature selection-based models in literature, the reported values of R and RMSE of the hybrid models in this thesis are considerably more accurate. Furthermore, prediction models based on the proposed ensemble feature selector outperform the models created by individually using the Relief-F, mRMR or MLFS, achieving similar or ideally up to 12.46% lower error rates on the average.…

paperback. Zustand: Gut. 192 Seiten; 9781627051972.3 Gewicht in Gramm: 500.

Taschenbuch. Zustand: Neu. Predicting Crude Oil Price in Nigeria with Machine Learning Models | Machine Learning Algorithms | Tayo Ogundunmade (u. a.) | Taschenbuch | Englisch | 2022 | LAP LAMBERT Academic Publishing | EAN 9786205511022 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. …

Zustand: New. In English.

Zustand: New. In English.

Taschenbuch. Zustand: Neu. Neuware.

Zustand: New. pp. 358.

Zustand: New. In English.

Zustand: New. In English.

Zustand: gut. 2013. Reasoning with Probabilistic and Deterministic Graphical Models: Exact Algorithms (Synthesis Lectures on Artificial Intelligence and Machine Learning, 23, Band 23) In englischer Sprache. pages.