Amit Dua (37 results)

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Soft cover. Condition: New. Description Should I choose supervised learning or reinforcement learning? Which algorithm is best suited for my application? How does deep learning advance the capacities of problem-solving? If you have found yourself asking these questions, this book is specially developed for you. The book will help readers understand the core concepts of machine learning and techniques to evaluate any machine learning model with ease. The book starts with the importance of machine learning by analyzing its impact on the global landscape. The book also covers Supervised and Unsupervised ML along with Reinforcement Learning. In subsequent chapters, the book explores these topics in even greater depth, evaluating the pros and cons of each and exploring important topics such as Bias-Variance Tradeoff, Clustering, and Dimensionality Reduction. The book also explains model evaluation techniques such as Cross-Validation and GridSearchCV. The book also features mind maps which help enhance the learning process by making it easier to learn and retain information. This book is a one-stop solution for covering basic ML concepts in detail and the perfect stepping stone to becoming an expert in ML and deep learning and even applying them to different professions. What you will learn ● Understand important concepts to fully grasp the idea of supervised learning. ● Get familiar with the basics of unsupervised learning and some of its algorithms. ● Learn how to analyze the performance of your Machine Learning models. ● Explore the different methodologies of Reinforcement Learning.…

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Paperback or Softback. Condition: New. Mastering Zero-knowledge Proofs: Practical study of security, scalability, and privacy in blockchain and modern systems (English Edition). Book.

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Taschenbuch. Condition: Neu. Candida Albicans and Complete Dentures - Mystery Unravelled. | Surface Adhesion and Penetration into Complete Dentures by Candida Albicans | Amit Dua (u. a.) | Taschenbuch | 76 S. | Englisch | 2011 | LAP LAMBERT Academic Publishing | EAN 9783843387958 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.…

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Paperback. Condition: new. Paperback. Volume I of a complete graduate course in time series analytics. Foundations and Linear Models in eighteen rigorous chapters, with theorems, full proofs, and runnable Python on real markets.This is the textbook the author wished he had when first standing in front of a quantitative graduate audience. The literature splits, awkwardly, into rigorous monographs that assume more probability theory than most applied students bring; econometrics texts that move past foundations to applications; and software-first introductions that get students producing forecasts but unable to defend a single line of the underlying derivation. Time Series Analytics: Theory and Python Practice sits in a different middle. Every chapter combines formal theory with working code.Volume I covers: Foundations (Chapters 1-8): stochastic processes; classical decomposition and STL; smoothing filters and exponential smoothing; the autocorrelation function; portmanteau tests for noise; variance-stabilising transformations.Linear models (Chapters 9-18): the Wold decomposition; backshift-operator calculus; the MA, AR, ARMA, ARIMA, and SARIMA families with full Box-Jenkins identification; transfer-function models; and two end-of-volume Python applications on Indian-equity data (MRF Limited daily ARIMA; NIFTY 50 monthly SARIMA).What makes this different: Two theorems per chapter, with full proofs in the Brockwell-Davis tradition. No "as the reader can show".Two worked examples per chapter: one synthetic so the student can verify by hand, one drawn from a real Indian-equity series - NIFTY 50, NIFTY Bank, MRF, Reliance Industries.Five graded discussion questions per chapter, with full worked solutions in the back-matter Solutions Appendix.Pinned numerical results. Every empirical claim is pinned to the exact output of a seeded Python run. Reproduce with np.random.seed(42).For graduate students in statistics, financial engineering, applied mathematics, and quantitative economics - and for the practitioner who wants both the theory and the working code in a single self-contained text.Prerequisites: probability through the central limit theorem; linear algebra including Cholesky decomposition; idiomatic NumPy and pandas. No prior exposure to statsmodels, arch, or yfinance is assumed.Volume II (sold separately) covers Estimation, Forecasting and Diagnostics; Multivariate and Volatility Models (VAR, Granger causality, IRFs, cointegration, ARCH/GARCH/EGARCH/GJR-GARCH and Value-at-Risk); and four end-to-end Python projects on Bitcoin volatility and a Gold-Bitcoin VAR.Approximately 495 pages - 18 chapters - 90 worked discussion-question solutions - dataset whitelist (NIFTY 50, NIFTY Bank, MRF, Reliance) - full reproducibility under np.random.seed(42). This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…