Coding theory design part (13 Ergebnisse)
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Zustand: Good. 1st Edition. Former library copy. Pages intact with minimal writing/highlighting. The binding may be loose and creased. Dust jackets/supplements are not included. Includes library markings. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.
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Taschenbuch. Zustand: Neu. Neuware - Crack the Machine Learning Interview - Part I is the essential starting point for anyone preparing for machine learning interviews seriously.If you've ever felt overwhelmed by the breadth of ML interview prep-math, statistics, algorithms, coding, system design, metrics, and modeling-this book… helps you build the right foundation first.This volume focuses on the core knowledge every strong machine learning candidate needs before moving into advanced modeling, deep learning, or system design.This book is designed for: - Machine Learning Engineers- Data Scientists- Applied Scientists- Research Engineers- Software Engineers transitioning into MLRather than giving you shallow definitions or generic textbook summaries, this book teaches you how to think about machine learning concepts the way interviewers expect: clearly, practically, and under real interview conditions.Inside Part I, you'll build a strong base in: - how machine learning interviews really work- how to build an interview preparation strategy that matches your role and timeline- the mathematics that matter most for ML interviews- the statistics concepts that interviewers repeatedly test- machine learning fundamentals and generalization- linear regression and logistic regression- decision trees, random forests, and gradient boosting- support vector machines, k-nearest neighbors, Naive Bayes, PCA, and clusteringThis book is especially useful if you want to: - strengthen weak fundamentals before interview season- move from passive ML knowledge to interview-ready explanation- understand not just what an algorithm is, but when to use it, what its trade-offs are, and how to explain it well- avoid common mistakes candidates make when answering foundational ML questions- build a base solid enough for coding rounds, system design rounds, and real-world ML problem solvingUnlike many interview prep books that jump straight into memorization, this volume emphasizes clarity, reasoning, and practical understanding. Every major topic is framed in the context of how it appears in interviews and how strong candidates are expected to explain it.If you want to become the kind of candidate who sounds thoughtful, technically grounded, and genuinely prepared-not just someone who memorized answers-this is where to start.Start with the foundation. Build the depth. Crack the machine learning interview.
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Taschenbuch. Zustand: Neu. Neuware - Crack the Machine Learning Interview - Part II takes you beyond the fundamentals and into the topics that separate average candidates from top-tier machine learning hires.If Part I builds your foundation, this volume helps you develop the depth, intuition, and technical strength needed to han…dle real interview challenges in modern ML roles.This book is designed for: - Machine Learning Engineers- Data Scientists- Applied Scientists- Deep Learning Engineers- Engineers preparing for advanced ML interview loopsIn Part II, you'll move into the core topics that frequently define mid-to-senior interview performance, including: - neural networks and backpropagation- training deep models effectively- optimization, regularization, and stability- CNNs, RNNs, and transformers- embeddings and representation learning- evaluation and metrics mastery- error analysis and debugging strategies- feature engineering and practical data workRather than treating these topics as abstract theory, this book teaches you how to think about them in the way interviewers expect: - how to explain deep learning concepts clearly- how to connect models to real-world use cases- how to reason about training failures and improvements- how to discuss trade-offs between models- how to demonstrate practical ML maturity under pressureThis book is especially useful if you want to: - move from basic ML knowledge to strong interview performance- confidently explain neural networks and deep learning concepts- understand optimization, regularization, and training behavior deeply- debug models and discuss failure modes like a real practitioner- handle follow-up questions in advanced ML interviewsUnlike many ML resources that focus only on definitions or code, this volume emphasizes clear thinking, strong intuition, and real interview communication.By the end of Part II, you will not just know deep learning-you will be able to explain it, defend your choices, and apply it under interview conditions.Go beyond fundamentals. Build real depth. Crack the machine learning interview.
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Taschenbuch. Zustand: Neu. Neuware - Crack the Machine Learning Interview - Part III is where theory becomes practice and candidates turn into real machine learning practitioners.After building your foundation and mastering advanced modeling, this volume focuses on the skills that truly differentiate strong ML candidates in real… interviews - debugging, evaluation, system design, and production thinking.This book is designed for: - Machine Learning Engineers- Data Scientists- Applied Scientists- Engineers preparing for system design and production-focused ML roles- Candidates targeting mid to senior-level interview loopsIn Part III, you will learn how to handle the types of questions that go beyond algorithms and directly reflect real-world ML work, including: - advanced evaluation metrics and trade-offs- error analysis and model debugging- feature engineering and data preprocessing in practice- experimentation and iterative model improvement- ML system design fundamentals- designing recommendation, ranking, and search systems- designing detection systems such as fraud and moderation- MLOps, deployment, and production reliabilityThis book helps you develop the mindset interviewers look for when they ask open-ended and practical machine learning questions: - how to think through ambiguous problems- how to debug models systematically- how to evaluate trade-offs in real-world systems- how to connect metrics to product outcomes- how to design scalable ML systems- how to explain decisions clearly under pressureThis volume is especially valuable if you want to: - move from theoretical knowledge to real ML reasoning- perform strongly in ML system design interviews- confidently answer open-ended and case-based questions- understand how models behave in production settings- show practical maturity during interviewsUnlike purely academic resources, this book focuses on how machine learning actually works in production and how interviewers expect you to reason about it.By the end of Part III, you will be able to think like a machine learning practitioner - not just someone who knows models, but someone who can design, debug, evaluate, and improve real systems.Think practically. Design intelligently. Crack the machine learning interview.
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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This IMA Volume in Mathematics and its Applications Coding Theory and Design Theory Part II: Design Theory is based on the proceedings of a workshop which was an integral part of the 1987-88 IMA program on APPLIED COMBINATORICS. We are grateful to th…e Scientific Committee: Victor Klee (Chairman), Daniel Kleitman, Dijen Ray-Chaudhuri and Dennis Stanton for planning and implementing an exciting and stimulating year long program. We especially thank the Workshop Organizer, Dijen Ray-Chaudhuri, for organizing a workshop which brought together many of the major figures in a variety of research fields in which coding theory and design theory are used. A vner Friedman Willard Miller, Jr. PREFACE Coding Theory and Design Theory are areas of Combinatorics which found rich applications of algebraic structures. Combinatorial designs are generalizations of finite geometries. Probably, the history of Design Theory begins with the 1847 pa per of Reverand T. P. Kirkman 'On a problem of Combinatorics', Cambridge and Dublin Math. Journal. The great Statistician R. A. Fisher reinvented the concept of combinatorial 2-design in the twentieth century. Extensive application of alge braic structures for construction of 2-designs (balanced incomplete block designs) can be found in RC. Bose's 1939 Annals of Eugenics paper, 'On the construction of balanced incomplete block designs'. Coding Theory and Design Theory are closely interconnected. Hamming codes can be found (in disguise) in RC. Bose's 1947 Sankhya paper 'Mathematical theory of the symmetrical factorial designs'.
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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This IMA Volume in Mathematics and its Applications Coding Theory and Design Theory Part I: Coding Theory is based on the proceedings of a workshop which was an integral part of the 1987-88 IMA program on APPLIED COMBINATORICS. We are grateful to the… Scientific Committee: Victor Klee (Chairman), Daniel Kleitman, Dijen Ray-Chaudhuri and Dennis Stanton for planning and implementing an exciting and stimulating year long program. We especially thank the Workshop Organizer, Dijen Ray-Chaudhuri, for organizing a workshop which brought together many of the major figures in a variety of research fields in which coding theory and design theory are used. A vner Friedman Willard Miller, Jr. PREFACE Coding Theory and Design Theory are areas of Combinatorics which found rich applications of algebraic structures. Combinatorial designs are generalizations of finite geometries. Probably, the history of Design Theory begins with the 1847 pa per of Reverand T. P. Kirkman 'On a problem of Combinatorics', Cambridge and Dublin Math. Journal. The great Statistician R. A. Fisher reinvented the concept of combinatorial 2-design in the twentieth century. Extensive application of alge braic structures for construction of 2-designs (balanced incomplete block designs) can be found in R. C. Bose's 1939 Annals of Eugenics paper, 'On the construction of balanced incomplete block designs'. Coding Theory and Design Theory are closely interconnected. Hamming codes can be found (in disguise) in R. C. Bose's 1947 Sankhya paper 'Mathematical theory of the symmetrical factorial designs'.
